Graph-Based Node Finding in Big Complex Contextual Social Graphs

Graph pattern matching is to find the subgraphs matching the given pattern graphs. In complex contextual social networks, considering the constraints of social contexts like the social relationships, the social trust, and the social positions, users are interested in the top-K matches of a specific node (denoted as the designated node) based on a pattern graph, rather than the entire set of graph matching. This inspires the conText-Aware Graph pattern-based top-K designated node matching (TAG-K) problem, which is NP-complete. Targeting this challenging problem, we propose a recurrent neural network- (RNN-) based Monte Carlo Tree Search algorithm (RN-MCTS), which automatically balances exploring new possible matches and extending existing matches. The RNN encodes the subgraph and maps it to a policy which is used to guide the MCTS. The experimental results demonstrate that our proposed algorithm outperforms the state-of-the-art methods in terms of both efficiency and effectiveness.

[1]  Hong Cheng,et al.  Finding top-k similar graphs in graph databases , 2012, EDBT '12.

[2]  Jeffrey Xu Yu,et al.  Optimal Enumeration: Efficient Top-k Tree Matching , 2015, Proc. VLDB Endow..

[3]  Csaba Szepesvári,et al.  Bandit Based Monte-Carlo Planning , 2006, ECML.

[4]  Xin Wang,et al.  Diversified Top-k Graph Pattern Matching , 2013, Proc. VLDB Endow..

[5]  Kai Zheng,et al.  Searching activity trajectory with keywords , 2018, World Wide Web.

[6]  Ernesto Nunes,et al.  Monte Carlo Tree Search for Multi-Robot Task Allocation , 2016, AAAI.

[7]  Peter Willett,et al.  Maximum common subgraph isomorphism algorithms for the matching of chemical structures , 2002, J. Comput. Aided Mol. Des..

[8]  Eytan Modiano,et al.  Traffic grooming in WDM networks , 2001, IEEE Commun. Mag..

[9]  Julian R. Ullmann,et al.  An Algorithm for Subgraph Isomorphism , 1976, J. ACM.

[10]  Meredith Ringel Morris,et al.  What do people ask their social networks, and why?: a survey study of status message q&a behavior , 2010, CHI.

[11]  Mehmet A. Orgun,et al.  Social Context-Aware Trust Network Discovery in Complex Contextual Social Networks , 2012, AAAI.

[12]  Peter Auer,et al.  Finite-time Analysis of the Multiarmed Bandit Problem , 2002, Machine Learning.

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

[14]  Kai Zheng,et al.  An efficient method for top-k graph based node matching , 2018, World Wide Web.

[15]  Jie Zhu,et al.  Time-Dependent Popular Routes Based Trajectory Outlier Detection , 2015, WISE.

[16]  Jianzhong Li,et al.  Graph pattern matching , 2010, Proc. VLDB Endow..

[17]  Jure Leskovec,et al.  Representation Learning on Graphs: Methods and Applications , 2017, IEEE Data Eng. Bull..

[18]  P. Berger,et al.  The Social Construction of Reality , 1966 .

[19]  Yang Li,et al.  Destination-Aware Task Assignment in Spatial Crowdsourcing: A Worker Decomposition Approach , 2020, IEEE Transactions on Knowledge and Data Engineering.

[20]  Hai Jin,et al.  Top-k Similarity Matching in Large Graphs with Attributes , 2014, DASFAA.

[21]  Jiajie Xu,et al.  Context-aware graph pattern based top-k designated nodes finding in social graphs , 2017, World Wide Web.

[22]  Kai Zheng,et al.  SharkDB: an in-memory column-oriented storage for trajectory analysis , 2018, World Wide Web.

[23]  Jignesh M. Patel,et al.  TALE: A Tool for Approximate Large Graph Matching , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[24]  Guanfeng Liu,et al.  TOSI: A trust-oriented social influence evaluation method in contextual social networks , 2016, Neurocomputing.

[25]  Jenny Benois-Pineau,et al.  Retrieval of objects in video by similarity based on graph matching , 2007, Pattern Recognit. Lett..

[26]  Guanfeng Liu,et al.  Reference-Based Framework for Spatio-Temporal Trajectory Compression and Query Processing , 2020, IEEE Transactions on Knowledge and Data Engineering.

[27]  Mehmet A. Orgun,et al.  Multi-Constrained Graph Pattern Matching in large-scale contextual social graphs , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[28]  Rémi Coulom,et al.  Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search , 2006, Computers and Games.

[29]  Gerhard Weikum,et al.  Efficient top-k querying over social-tagging networks , 2008, SIGIR '08.

[30]  Mehmet A. Orgun,et al.  Optimal Social Trust Path Selection in Complex Social Networks , 2010, AAAI.

[31]  Jan Vondrák,et al.  On Principles of Egocentric Person Search in Social Networks , 2011, VLDS.

[32]  Christian S. Jensen,et al.  Answering Why-Not Group Spatial Keyword Queries , 2020, IEEE Transactions on Knowledge and Data Engineering.

[33]  Michael Garland,et al.  Implementing sparse matrix-vector multiplication on throughput-oriented processors , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.