Learning Scale-Free Networks by Dynamic Node Specific Degree Prior

Qingming Tang, Siqi Sun, Jinbo Xu
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2247-2255, 2015.

Abstract

Learning network structure underlying data is an important problem in machine learning. This paper presents a novel degree prior to study the inference of scale-free networks, which are widely used to model social and biological networks. In particular, this paper formulates scale-free network inference using Gaussian Graphical model (GGM) regularized by a node degree prior. Our degree prior not only promotes a desirable global degree distribution, but also exploits the estimated degree of an individual node and the relative strength of all the edges of a single node. To fulfill this, this paper proposes a ranking-based method to dynamically estimate the degree of a node, which makes the resultant optimization problem challenging to solve. To deal with this, this paper presents a novel ADMM (alternating direction method of multipliers) procedure. Our experimental results on both synthetic and real data show that our prior not only yields a scale-free network, but also produces many more correctly predicted edges than existing scale-free inducing prior, hub-inducing prior and the l_1 norm.

Cite this Paper

BibTeX
@InProceedings{pmlr-v37-tangb15, title = {Learning Scale-Free Networks by Dynamic Node Specific Degree Prior}, author = {Tang, Qingming and Sun, Siqi and Xu, Jinbo}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, pages = {2247--2255}, year = {2015}, editor = {Bach, Francis and Blei, David}, volume = {37}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v37/tangb15.pdf}, url = {https://proceedings.mlr.press/v37/tangb15.html}, abstract = {Learning network structure underlying data is an important problem in machine learning. This paper presents a novel degree prior to study the inference of scale-free networks, which are widely used to model social and biological networks. In particular, this paper formulates scale-free network inference using Gaussian Graphical model (GGM) regularized by a node degree prior. Our degree prior not only promotes a desirable global degree distribution, but also exploits the estimated degree of an individual node and the relative strength of all the edges of a single node. To fulfill this, this paper proposes a ranking-based method to dynamically estimate the degree of a node, which makes the resultant optimization problem challenging to solve. To deal with this, this paper presents a novel ADMM (alternating direction method of multipliers) procedure. Our experimental results on both synthetic and real data show that our prior not only yields a scale-free network, but also produces many more correctly predicted edges than existing scale-free inducing prior, hub-inducing prior and the l_1 norm.} }
Endnote
%0 Conference Paper %T Learning Scale-Free Networks by Dynamic Node Specific Degree Prior %A Qingming Tang %A Siqi Sun %A Jinbo Xu %B Proceedings of the 32nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Francis Bach %E David Blei %F pmlr-v37-tangb15 %I PMLR %P 2247--2255 %U https://proceedings.mlr.press/v37/tangb15.html %V 37 %X Learning network structure underlying data is an important problem in machine learning. This paper presents a novel degree prior to study the inference of scale-free networks, which are widely used to model social and biological networks. In particular, this paper formulates scale-free network inference using Gaussian Graphical model (GGM) regularized by a node degree prior. Our degree prior not only promotes a desirable global degree distribution, but also exploits the estimated degree of an individual node and the relative strength of all the edges of a single node. To fulfill this, this paper proposes a ranking-based method to dynamically estimate the degree of a node, which makes the resultant optimization problem challenging to solve. To deal with this, this paper presents a novel ADMM (alternating direction method of multipliers) procedure. Our experimental results on both synthetic and real data show that our prior not only yields a scale-free network, but also produces many more correctly predicted edges than existing scale-free inducing prior, hub-inducing prior and the l_1 norm.
RIS
TY - CPAPER TI - Learning Scale-Free Networks by Dynamic Node Specific Degree Prior AU - Qingming Tang AU - Siqi Sun AU - Jinbo Xu BT - Proceedings of the 32nd International Conference on Machine Learning DA - 2015/06/01 ED - Francis Bach ED - David Blei ID - pmlr-v37-tangb15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 37 SP - 2247 EP - 2255 L1 - http://proceedings.mlr.press/v37/tangb15.pdf UR - https://proceedings.mlr.press/v37/tangb15.html AB - Learning network structure underlying data is an important problem in machine learning. This paper presents a novel degree prior to study the inference of scale-free networks, which are widely used to model social and biological networks. In particular, this paper formulates scale-free network inference using Gaussian Graphical model (GGM) regularized by a node degree prior. Our degree prior not only promotes a desirable global degree distribution, but also exploits the estimated degree of an individual node and the relative strength of all the edges of a single node. To fulfill this, this paper proposes a ranking-based method to dynamically estimate the degree of a node, which makes the resultant optimization problem challenging to solve. To deal with this, this paper presents a novel ADMM (alternating direction method of multipliers) procedure. Our experimental results on both synthetic and real data show that our prior not only yields a scale-free network, but also produces many more correctly predicted edges than existing scale-free inducing prior, hub-inducing prior and the l_1 norm. ER -
APA
Tang, Q., Sun, S. & Xu, J.. (2015). Learning Scale-Free Networks by Dynamic Node Specific Degree Prior. Proceedings of the 32nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 37:2247-2255 Available from https://proceedings.mlr.press/v37/tangb15.html.

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