Time-varying graph learning from smooth and stationary graph signals with hidden nodes

Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a s...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ye, Rong [verfasserIn] Jiang, Xue-Qin Feng, Hui Wang, Jian Qiu, Runhe Hou, Xinxin

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Graph signal processing Graph learning Time-varying graphs Hidden nodes Graph stationary Column-sparsity

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: EURASIP journal on advances in signal processing - Springer International Publishing, 2007, 2024(2024), 1 vom: 13. März
Übergeordnetes Werk:	volume:2024 ; year:2024 ; number:1 ; day:13 ; month:03

Links:	Volltext

DOI / URN:	10.1186/s13634-024-01128-0

Katalog-ID:	SPR055137164

Internformat


LEADER	01000naa a22002652 4500
001	SPR055137164
003	DE-627
005	20240315064821.0
007	cr uuu---uuuuu
008	240315s2024 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1186/s13634-024-01128-0 \|2 doi
035			\|a (DE-627)SPR055137164
035			\|a (SPR)s13634-024-01128-0-e
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
082	0	4	\|a 620 \|q VZ
084			\|a 53.73 \|2 bkl
100	1		\|a Ye, Rong \|e verfasserin \|0 (orcid)0009-0008-8704-4925 \|4 aut
245	1	0	\|a Time-varying graph learning from smooth and stationary graph signals with hidden nodes
264		1	\|c 2024
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
500			\|a © The Author(s) 2024
520			\|a Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods.
650		4	\|a Graph signal processing \|7 (dpeaa)DE-He213
650		4	\|a Graph learning \|7 (dpeaa)DE-He213
650		4	\|a Time-varying graphs \|7 (dpeaa)DE-He213
650		4	\|a Hidden nodes \|7 (dpeaa)DE-He213
650		4	\|a Graph stationary \|7 (dpeaa)DE-He213
650		4	\|a Column-sparsity \|7 (dpeaa)DE-He213
700	1		\|a Jiang, Xue-Qin \|4 aut
700	1		\|a Feng, Hui \|4 aut
700	1		\|a Wang, Jian \|4 aut
700	1		\|a Qiu, Runhe \|4 aut
700	1		\|a Hou, Xinxin \|4 aut
773	0	8	\|i Enthalten in \|t EURASIP journal on advances in signal processing \|d Springer International Publishing, 2007 \|g 2024(2024), 1 vom: 13. März \|w (DE-627)534054277 \|w (DE-600)2364203-8 \|x 1687-6180 \|7 nnns
773	1	8	\|g volume:2024 \|g year:2024 \|g number:1 \|g day:13 \|g month:03
856	4	0	\|u https://dx.doi.org/10.1186/s13634-024-01128-0 \|z kostenfrei \|3 Volltext
912			\|a SYSFLAG_0
912			\|a GBV_SPRINGER
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
936	b	k	\|a 53.73 \|q VZ
951			\|a AR
952			\|d 2024 \|j 2024 \|e 1 \|b 13 \|c 03

Indexfelder

author_variant	r y ry x q j xqj h f hf j w jw r q rq x h xh
matchkey_str	article:16876180:2024----::ieayngaherigrmmohnsainrgahi
hierarchy_sort_str	2024
bklnumber	53.73
publishDate	2024
allfields	10.1186/s13634-024-01128-0 doi (DE-627)SPR055137164 (SPR)s13634-024-01128-0-e DE-627 ger DE-627 rakwb eng 620 VZ 53.73 bkl Ye, Rong verfasserin (orcid)0009-0008-8704-4925 aut Time-varying graph learning from smooth and stationary graph signals with hidden nodes 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods. Graph signal processing (dpeaa)DE-He213 Graph learning (dpeaa)DE-He213 Time-varying graphs (dpeaa)DE-He213 Hidden nodes (dpeaa)DE-He213 Graph stationary (dpeaa)DE-He213 Column-sparsity (dpeaa)DE-He213 Jiang, Xue-Qin aut Feng, Hui aut Wang, Jian aut Qiu, Runhe aut Hou, Xinxin aut Enthalten in EURASIP journal on advances in signal processing Springer International Publishing, 2007 2024(2024), 1 vom: 13. März (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2024 year:2024 number:1 day:13 month:03 https://dx.doi.org/10.1186/s13634-024-01128-0 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 VZ AR 2024 2024 1 13 03
spelling	10.1186/s13634-024-01128-0 doi (DE-627)SPR055137164 (SPR)s13634-024-01128-0-e DE-627 ger DE-627 rakwb eng 620 VZ 53.73 bkl Ye, Rong verfasserin (orcid)0009-0008-8704-4925 aut Time-varying graph learning from smooth and stationary graph signals with hidden nodes 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods. Graph signal processing (dpeaa)DE-He213 Graph learning (dpeaa)DE-He213 Time-varying graphs (dpeaa)DE-He213 Hidden nodes (dpeaa)DE-He213 Graph stationary (dpeaa)DE-He213 Column-sparsity (dpeaa)DE-He213 Jiang, Xue-Qin aut Feng, Hui aut Wang, Jian aut Qiu, Runhe aut Hou, Xinxin aut Enthalten in EURASIP journal on advances in signal processing Springer International Publishing, 2007 2024(2024), 1 vom: 13. März (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2024 year:2024 number:1 day:13 month:03 https://dx.doi.org/10.1186/s13634-024-01128-0 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 VZ AR 2024 2024 1 13 03
allfields_unstemmed	10.1186/s13634-024-01128-0 doi (DE-627)SPR055137164 (SPR)s13634-024-01128-0-e DE-627 ger DE-627 rakwb eng 620 VZ 53.73 bkl Ye, Rong verfasserin (orcid)0009-0008-8704-4925 aut Time-varying graph learning from smooth and stationary graph signals with hidden nodes 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods. Graph signal processing (dpeaa)DE-He213 Graph learning (dpeaa)DE-He213 Time-varying graphs (dpeaa)DE-He213 Hidden nodes (dpeaa)DE-He213 Graph stationary (dpeaa)DE-He213 Column-sparsity (dpeaa)DE-He213 Jiang, Xue-Qin aut Feng, Hui aut Wang, Jian aut Qiu, Runhe aut Hou, Xinxin aut Enthalten in EURASIP journal on advances in signal processing Springer International Publishing, 2007 2024(2024), 1 vom: 13. März (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2024 year:2024 number:1 day:13 month:03 https://dx.doi.org/10.1186/s13634-024-01128-0 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 VZ AR 2024 2024 1 13 03
allfieldsGer	10.1186/s13634-024-01128-0 doi (DE-627)SPR055137164 (SPR)s13634-024-01128-0-e DE-627 ger DE-627 rakwb eng 620 VZ 53.73 bkl Ye, Rong verfasserin (orcid)0009-0008-8704-4925 aut Time-varying graph learning from smooth and stationary graph signals with hidden nodes 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods. Graph signal processing (dpeaa)DE-He213 Graph learning (dpeaa)DE-He213 Time-varying graphs (dpeaa)DE-He213 Hidden nodes (dpeaa)DE-He213 Graph stationary (dpeaa)DE-He213 Column-sparsity (dpeaa)DE-He213 Jiang, Xue-Qin aut Feng, Hui aut Wang, Jian aut Qiu, Runhe aut Hou, Xinxin aut Enthalten in EURASIP journal on advances in signal processing Springer International Publishing, 2007 2024(2024), 1 vom: 13. März (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2024 year:2024 number:1 day:13 month:03 https://dx.doi.org/10.1186/s13634-024-01128-0 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 VZ AR 2024 2024 1 13 03
allfieldsSound	10.1186/s13634-024-01128-0 doi (DE-627)SPR055137164 (SPR)s13634-024-01128-0-e DE-627 ger DE-627 rakwb eng 620 VZ 53.73 bkl Ye, Rong verfasserin (orcid)0009-0008-8704-4925 aut Time-varying graph learning from smooth and stationary graph signals with hidden nodes 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods. Graph signal processing (dpeaa)DE-He213 Graph learning (dpeaa)DE-He213 Time-varying graphs (dpeaa)DE-He213 Hidden nodes (dpeaa)DE-He213 Graph stationary (dpeaa)DE-He213 Column-sparsity (dpeaa)DE-He213 Jiang, Xue-Qin aut Feng, Hui aut Wang, Jian aut Qiu, Runhe aut Hou, Xinxin aut Enthalten in EURASIP journal on advances in signal processing Springer International Publishing, 2007 2024(2024), 1 vom: 13. März (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2024 year:2024 number:1 day:13 month:03 https://dx.doi.org/10.1186/s13634-024-01128-0 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 VZ AR 2024 2024 1 13 03
language	English
source	Enthalten in EURASIP journal on advances in signal processing 2024(2024), 1 vom: 13. März volume:2024 year:2024 number:1 day:13 month:03
sourceStr	Enthalten in EURASIP journal on advances in signal processing 2024(2024), 1 vom: 13. März volume:2024 year:2024 number:1 day:13 month:03
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Graph signal processing Graph learning Time-varying graphs Hidden nodes Graph stationary Column-sparsity
dewey-raw	620
isfreeaccess_bool	true
container_title	EURASIP journal on advances in signal processing
authorswithroles_txt_mv	Ye, Rong @@aut@@ Jiang, Xue-Qin @@aut@@ Feng, Hui @@aut@@ Wang, Jian @@aut@@ Qiu, Runhe @@aut@@ Hou, Xinxin @@aut@@
publishDateDaySort_date	2024-03-13T00:00:00Z
hierarchy_top_id	534054277
dewey-sort	3620
id	SPR055137164
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR055137164</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240315064821.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240315s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13634-024-01128-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR055137164</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13634-024-01128-0-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ye, Rong</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0009-0008-8704-4925</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Time-varying graph learning from smooth and stationary graph signals with hidden nodes</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2024</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph signal processing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time-varying graphs</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hidden nodes</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph stationary</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Column-sparsity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiang, Xue-Qin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Feng, Hui</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Jian</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qiu, Runhe</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hou, Xinxin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">EURASIP journal on advances in signal processing</subfield><subfield code="d">Springer International Publishing, 2007</subfield><subfield code="g">2024(2024), 1 vom: 13. März</subfield><subfield code="w">(DE-627)534054277</subfield><subfield code="w">(DE-600)2364203-8</subfield><subfield code="x">1687-6180</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:2024</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:1</subfield><subfield code="g">day:13</subfield><subfield code="g">month:03</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s13634-024-01128-0</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.73</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">2024</subfield><subfield code="j">2024</subfield><subfield code="e">1</subfield><subfield code="b">13</subfield><subfield code="c">03</subfield></datafield></record></collection>
author	Ye, Rong
spellingShingle	Ye, Rong ddc 620 bkl 53.73 misc Graph signal processing misc Graph learning misc Time-varying graphs misc Hidden nodes misc Graph stationary misc Column-sparsity Time-varying graph learning from smooth and stationary graph signals with hidden nodes
authorStr	Ye, Rong
ppnlink_with_tag_str_mv	@@773@@(DE-627)534054277
format	electronic Article
dewey-ones	620 - Engineering & allied operations
delete_txt_mv	keep
author_role	aut aut aut aut aut aut
collection	springer
remote_str	true
illustrated	Not Illustrated
issn	1687-6180
topic_title	620 VZ 53.73 bkl Time-varying graph learning from smooth and stationary graph signals with hidden nodes Graph signal processing (dpeaa)DE-He213 Graph learning (dpeaa)DE-He213 Time-varying graphs (dpeaa)DE-He213 Hidden nodes (dpeaa)DE-He213 Graph stationary (dpeaa)DE-He213 Column-sparsity (dpeaa)DE-He213
topic	ddc 620 bkl 53.73 misc Graph signal processing misc Graph learning misc Time-varying graphs misc Hidden nodes misc Graph stationary misc Column-sparsity
topic_unstemmed	ddc 620 bkl 53.73 misc Graph signal processing misc Graph learning misc Time-varying graphs misc Hidden nodes misc Graph stationary misc Column-sparsity
topic_browse	ddc 620 bkl 53.73 misc Graph signal processing misc Graph learning misc Time-varying graphs misc Hidden nodes misc Graph stationary misc Column-sparsity
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	EURASIP journal on advances in signal processing
hierarchy_parent_id	534054277
dewey-tens	620 - Engineering
hierarchy_top_title	EURASIP journal on advances in signal processing
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)534054277 (DE-600)2364203-8
title	Time-varying graph learning from smooth and stationary graph signals with hidden nodes
ctrlnum	(DE-627)SPR055137164 (SPR)s13634-024-01128-0-e
title_full	Time-varying graph learning from smooth and stationary graph signals with hidden nodes
author_sort	Ye, Rong
journal	EURASIP journal on advances in signal processing
journalStr	EURASIP journal on advances in signal processing
lang_code	eng
isOA_bool	true
dewey-hundreds	600 - Technology
recordtype	marc
publishDateSort	2024
contenttype_str_mv	txt
author_browse	Ye, Rong Jiang, Xue-Qin Feng, Hui Wang, Jian Qiu, Runhe Hou, Xinxin
container_volume	2024
class	620 VZ 53.73 bkl
format_se	Elektronische Aufsätze
author-letter	Ye, Rong
doi_str_mv	10.1186/s13634-024-01128-0
normlink	(ORCID)0009-0008-8704-4925
normlink_prefix_str_mv	(orcid)0009-0008-8704-4925
dewey-full	620
title_sort	time-varying graph learning from smooth and stationary graph signals with hidden nodes
title_auth	Time-varying graph learning from smooth and stationary graph signals with hidden nodes
abstract	Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods. © The Author(s) 2024
abstractGer	Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods. © The Author(s) 2024
abstract_unstemmed	Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods. © The Author(s) 2024
collection_details	SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	1
title_short	Time-varying graph learning from smooth and stationary graph signals with hidden nodes
url	https://dx.doi.org/10.1186/s13634-024-01128-0
remote_bool	true
author2	Jiang, Xue-Qin Feng, Hui Wang, Jian Qiu, Runhe Hou, Xinxin
author2Str	Jiang, Xue-Qin Feng, Hui Wang, Jian Qiu, Runhe Hou, Xinxin
ppnlink	534054277
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1186/s13634-024-01128-0
up_date	2024-07-03T13:36:51.845Z
_version_	1803565202770231296
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR055137164</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240315064821.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240315s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13634-024-01128-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR055137164</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13634-024-01128-0-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ye, Rong</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0009-0008-8704-4925</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Time-varying graph learning from smooth and stationary graph signals with hidden nodes</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2024</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph signal processing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time-varying graphs</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hidden nodes</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph stationary</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Column-sparsity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiang, Xue-Qin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Feng, Hui</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Jian</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qiu, Runhe</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hou, Xinxin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">EURASIP journal on advances in signal processing</subfield><subfield code="d">Springer International Publishing, 2007</subfield><subfield code="g">2024(2024), 1 vom: 13. März</subfield><subfield code="w">(DE-627)534054277</subfield><subfield code="w">(DE-600)2364203-8</subfield><subfield code="x">1687-6180</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:2024</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:1</subfield><subfield code="g">day:13</subfield><subfield code="g">month:03</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s13634-024-01128-0</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.73</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">2024</subfield><subfield code="j">2024</subfield><subfield code="e">1</subfield><subfield code="b">13</subfield><subfield code="c">03</subfield></datafield></record></collection>
score	7.4015865

Nicht das Richtige dabei?

Schreiben Sie uns!

Time-varying graph learning from smooth and stationary graph signals with hidden nodes

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?