Multilabel Classification on Heterogeneous Graphs with Gaussian Embeddings

We consider the problem of node classification in heterogeneous graphs, where both nodes and relations may be of different types, and different sets of categories are associated to each node type. While graph node classification has mainly been tackled for homogeneous graphs, heterogeneous classification is a recent problem which has been motivated by applications in fields such as social networks, where graphs are intrinsically heterogeneous. We propose a transductive approach to this problem based on learning graph embeddings, and model the uncertainty associated to the node representations using Gaussian embeddings. A comparison with representative baselines is provided on three heterogeneous datasets.

[1]  Gerhard Weikum,et al.  Graffiti: graph-based classification in heterogeneous networks , 2011, World Wide Web.

[2]  Zoubin Ghahramani,et al.  Learning from labeled and unlabeled data with label propagation , 2002 .

[3]  Jennifer Neville,et al.  Iterative Classification in Relational Data , 2000 .

[4]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[5]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[6]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[7]  Bernhard Schölkopf,et al.  Learning from labeled and unlabeled data on a directed graph , 2005, ICML.

[8]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Jason Weston,et al.  Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.

[10]  Gyana R. Parija,et al.  Learning to Propagate Rare Labels , 2014, CIKM.

[11]  Ling Liu,et al.  Activity-edge centric multi-label classification for mining heterogeneous information networks , 2014, KDD.

[12]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[13]  Sung-Hyon Myaeng,et al.  A practical hypertext catergorization method using links and incrementally available class information , 2000, SIGIR '00.

[14]  Jun Zhao,et al.  Learning to Represent Knowledge Graphs with Gaussian Embedding , 2015, CIKM.

[15]  Alexander Zien,et al.  Label Propagation and Quadratic Criterion , 2006 .

[16]  Masashi Sugiyama,et al.  Integration of Multiple Networks for Robust Label Propagation , 2008, SDM.

[17]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[18]  Paul N. Bennett,et al.  Overcoming Relational Learning Biases to Accurately Predict Preferences in Large Scale Networks , 2015, WWW.

[19]  Jason Weston,et al.  Learning Structured Embeddings of Knowledge Bases , 2011, AAAI.

[20]  Ludovic Denoyer,et al.  Classification and annotation in social corpora using multiple relations , 2011, CIKM '11.

[21]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[22]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[23]  Lise Getoor,et al.  Collective Classification in Network Data , 2008, AI Mag..

[24]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[25]  Tommi S. Jaakkola,et al.  Partially labeled classification with Markov random walks , 2001, NIPS.

[26]  Ludovic Denoyer,et al.  Learning latent representations of nodes for classifying in heterogeneous social networks , 2014, WSDM.

[27]  Yizhou Sun,et al.  Graph Regularized Transductive Classification on Heterogeneous Information Networks , 2010, ECML/PKDD.