iNEAT: Incomplete Network Alignment

Network alignment and network completion are two fundamental cornerstones behind many high-impact graph mining applications. The state-of-the-arts have been addressing these tasks in parallel. In this paper, we argue that network alignment and completion are inherently complementary with each other, and hence propose to jointly address them so that the two tasks can benefit from each other. We formulate it from the optimization perspective, and propose an effective algorithm iNEAT to solve it. The proposed method offers two distinctive advantages. First (Alignment accuracy), our method benefits from higher-quality input networks while mitigates the effect of incorrectly inferred links introduced by the completion task itself. Second (Alignment efficiency), thanks to the low-rank structure of the complete networks and alignment matrix, the alignment can be significantly accelerated. The extensive experiments demonstrate the performance of our algorithm.

[1]  Hanghang Tong,et al.  FIRST: Fast Interactive Attributed Subgraph Matching , 2017, KDD.

[2]  Philip S. Yu,et al.  COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency , 2015, KDD.

[3]  Hanghang Tong,et al.  Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion , 2014, SDM.

[4]  Hanghang Tong,et al.  FINAL: Fast Attributed Network Alignment , 2016, KDD.

[5]  Bonnie Berger,et al.  Global alignment of multiple protein interaction networks with application to functional orthology detection , 2008, Proceedings of the National Academy of Sciences.

[6]  Jure Leskovec,et al.  Defining and evaluating network communities based on ground-truth , 2012, Knowledge and Information Systems.

[7]  Ying Wang,et al.  Message-Passing Algorithms for Sparse Network Alignment , 2009, TKDD.

[8]  Emmanuel J. Candès,et al.  A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..

[9]  Chris H. Q. Ding,et al.  On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering , 2005, SDM.

[10]  Christoph Schnörr,et al.  Probabilistic Subgraph Matching Based on Convex Relaxation , 2005, EMMCVPR.

[11]  Reza Zafarani,et al.  Connecting users across social media sites: a behavioral-modeling approach , 2013, KDD.

[12]  Danai Koutra,et al.  BIG-ALIGN: Fast Bipartite Graph Alignment , 2013, 2013 IEEE 13th International Conference on Data Mining.

[13]  S. Yun,et al.  An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems , 2009 .

[14]  Jure Leskovec,et al.  Learning to Discover Social Circles in Ego Networks , 2012, NIPS.

[15]  Jingrui He,et al.  Crowdsourcing via Tensor Augmentation and Completion , 2016, IJCAI.

[16]  Christos Faloutsos,et al.  Graph evolution: Densification and shrinking diameters , 2006, TKDD.

[17]  Hayder Radha,et al.  Network completion with node similarity: A matrix completion approach with provable guarantees , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[18]  Kaare Brandt Petersen,et al.  The Matrix Cookbook , 2006 .

[19]  Jingrui He,et al.  A Local Algorithm for Structure-Preserving Graph Cut , 2017, KDD.

[20]  Pierre-Antoine Absil,et al.  RTRMC: A Riemannian trust-region method for low-rank matrix completion , 2011, NIPS.

[21]  Jieping Ye,et al.  Tensor Completion for Estimating Missing Values in Visual Data , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Benjamin Recht,et al.  A Simpler Approach to Matrix Completion , 2009, J. Mach. Learn. Res..

[23]  Jure Leskovec,et al.  The Network Completion Problem: Inferring Missing Nodes and Edges in Networks , 2011, SDM.

[24]  Nathan Srebro,et al.  Fast maximum margin matrix factorization for collaborative prediction , 2005, ICML.

[25]  Erhard Rahm,et al.  Similarity flooding: a versatile graph matching algorithm and its application to schema matching , 2002, Proceedings 18th International Conference on Data Engineering.

[26]  Bonnie Berger,et al.  IsoRankN: spectral methods for global alignment of multiple protein networks , 2009, Bioinform..

[27]  Philip S. Yu,et al.  Multiple Anonymized Social Networks Alignment , 2015, 2015 IEEE International Conference on Data Mining.