A bag-of-paths framework for network data analysis
This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Françoisse, Kevin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
Distance and similarity on a graph |
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| Umfang: |
22 |
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| Übergeordnetes Werk: |
Enthalten in: Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing - 2012, the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:90 ; year:2017 ; pages:90-111 ; extent:22 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.neunet.2017.03.010 |
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ELV035994088 |
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| 520 | |a This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. | ||
| 520 | |a This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. | ||
| 650 | 7 | |a Link analysis |2 Elsevier | |
| 650 | 7 | |a Resistance distance |2 Elsevier | |
| 650 | 7 | |a Distance and similarity on a graph |2 Elsevier | |
| 650 | 7 | |a Semi-supervised classification |2 Elsevier | |
| 650 | 7 | |a Network science |2 Elsevier | |
| 650 | 7 | |a Commute-time distance |2 Elsevier | |
| 700 | 1 | |a Kivimäki, Ilkka |4 oth | |
| 700 | 1 | |a Mantrach, Amin |4 oth | |
| 700 | 1 | |a Rossi, Fabrice |4 oth | |
| 700 | 1 | |a Saerens, Marco |4 oth | |
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10.1016/j.neunet.2017.03.010 doi GBVA2017014000027.pica (DE-627)ELV035994088 (ELSEVIER)S0893-6080(17)30066-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Françoisse, Kevin verfasserin aut A bag-of-paths framework for network data analysis 2017transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. Link analysis Elsevier Resistance distance Elsevier Distance and similarity on a graph Elsevier Semi-supervised classification Elsevier Network science Elsevier Commute-time distance Elsevier Kivimäki, Ilkka oth Mantrach, Amin oth Rossi, Fabrice oth Saerens, Marco oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:90 year:2017 pages:90-111 extent:22 https://doi.org/10.1016/j.neunet.2017.03.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 90 2017 90-111 22 045F 004 |
| spelling |
10.1016/j.neunet.2017.03.010 doi GBVA2017014000027.pica (DE-627)ELV035994088 (ELSEVIER)S0893-6080(17)30066-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Françoisse, Kevin verfasserin aut A bag-of-paths framework for network data analysis 2017transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. Link analysis Elsevier Resistance distance Elsevier Distance and similarity on a graph Elsevier Semi-supervised classification Elsevier Network science Elsevier Commute-time distance Elsevier Kivimäki, Ilkka oth Mantrach, Amin oth Rossi, Fabrice oth Saerens, Marco oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:90 year:2017 pages:90-111 extent:22 https://doi.org/10.1016/j.neunet.2017.03.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 90 2017 90-111 22 045F 004 |
| allfields_unstemmed |
10.1016/j.neunet.2017.03.010 doi GBVA2017014000027.pica (DE-627)ELV035994088 (ELSEVIER)S0893-6080(17)30066-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Françoisse, Kevin verfasserin aut A bag-of-paths framework for network data analysis 2017transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. Link analysis Elsevier Resistance distance Elsevier Distance and similarity on a graph Elsevier Semi-supervised classification Elsevier Network science Elsevier Commute-time distance Elsevier Kivimäki, Ilkka oth Mantrach, Amin oth Rossi, Fabrice oth Saerens, Marco oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:90 year:2017 pages:90-111 extent:22 https://doi.org/10.1016/j.neunet.2017.03.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 90 2017 90-111 22 045F 004 |
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10.1016/j.neunet.2017.03.010 doi GBVA2017014000027.pica (DE-627)ELV035994088 (ELSEVIER)S0893-6080(17)30066-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Françoisse, Kevin verfasserin aut A bag-of-paths framework for network data analysis 2017transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. Link analysis Elsevier Resistance distance Elsevier Distance and similarity on a graph Elsevier Semi-supervised classification Elsevier Network science Elsevier Commute-time distance Elsevier Kivimäki, Ilkka oth Mantrach, Amin oth Rossi, Fabrice oth Saerens, Marco oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:90 year:2017 pages:90-111 extent:22 https://doi.org/10.1016/j.neunet.2017.03.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 90 2017 90-111 22 045F 004 |
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10.1016/j.neunet.2017.03.010 doi GBVA2017014000027.pica (DE-627)ELV035994088 (ELSEVIER)S0893-6080(17)30066-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Françoisse, Kevin verfasserin aut A bag-of-paths framework for network data analysis 2017transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. Link analysis Elsevier Resistance distance Elsevier Distance and similarity on a graph Elsevier Semi-supervised classification Elsevier Network science Elsevier Commute-time distance Elsevier Kivimäki, Ilkka oth Mantrach, Amin oth Rossi, Fabrice oth Saerens, Marco oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:90 year:2017 pages:90-111 extent:22 https://doi.org/10.1016/j.neunet.2017.03.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 90 2017 90-111 22 045F 004 |
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Enthalten in Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing Amsterdam volume:90 year:2017 pages:90-111 extent:22 |
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Enthalten in Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing Amsterdam volume:90 year:2017 pages:90-111 extent:22 |
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Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing |
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Françoisse, Kevin @@aut@@ Kivimäki, Ilkka @@oth@@ Mantrach, Amin @@oth@@ Rossi, Fabrice @@oth@@ Saerens, Marco @@oth@@ |
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a bag-of-paths framework for network data analysis |
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A bag-of-paths framework for network data analysis |
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This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. |
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This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. |
| abstract_unstemmed |
This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs–Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness, or more precisely accessibility, between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest-path distance and the commute-cost distance. In addition, it extends the Bellman–Ford formula for computing the shortest-path distance in order to integrate sub-optimal paths (exploration) by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification tasks show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures. |
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