A Comparative Study of Subgraph Matching Isomorphic Methods in Social Networks

With the fast development of social networks, more and more data has been generated. Finding useful information among these data is important. Subgraph matching is the method that can be used in social networks for social search, recommender systems, and so on. In this paper, we choose five typical graph matching methods which are VF2, SPath, TurboISO, BoostIso, and RI to value their performance and scalability in social networks. These methods are verified by three social network data sets whose node’s number vary from thousands to millions. According to the experiments, we can find that VF2 and RI is applicable for rather small graphs while SPath performs better in large graph when average degree of graph is small. When graph with high average degree, TurboISO and BoostIso performs better. What is more, we also compare the efficiency of different matching order when searching. The order choosing strategy proposed by RI is better.

[1]  Tinghuai Ma,et al.  An efficient and scalable density-based clustering algorithm for datasets with complex structures , 2016, Neurocomputing.

[2]  Xiao Zi-hong.  Research and Application on Crime Rule Based on Graph Data Mining Algorithm , 2011 .

[3]  Seung-won Hwang,et al.  Supporting Pattern-Matching Queries over Trajectories on Road Networks , 2011, IEEE Transactions on Knowledge and Data Engineering.

[4]  Quan Z. Sheng,et al.  Efficient pattern matching for graphs with multi-Labeled nodes , 2016, Knowl. Based Syst..

[5]  Behnam Neyshabur,et al.  NETAL: a new graph-based method for global alignment of protein-protein interaction networks , 2013, Bioinform..

[6]  Benjamin I. P. Rubinstein,et al.  Principled Graph Matching Algorithms for Integrating Multiple Data Sources , 2014, IEEE Transactions on Knowledge and Data Engineering.

[7]  Jeong-Hoon Lee,et al.  An In-depth Comparison of Subgraph Isomorphism Algorithms in Graph Databases , 2012, Proc. VLDB Endow..

[8]  Volkmar Frinken,et al.  Approximation of graph edit distance based on Hausdorff matching , 2015, Pattern Recognit..

[9]  Andy King,et al.  Optimising the Volgenant-Jonker algorithm for approximating graph edit distance , 2017, Pattern Recognit. Lett..

[10]  Lei Zou,et al.  Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs , 2018, IEEE Transactions on Knowledge and Data Engineering.

[11]  Jure Leskovec,et al.  {SNAP Datasets}: {Stanford} Large Network Dataset Collection , 2014 .

[12]  Weili Wu,et al.  Efficient algorithms for supergraph query processing on graph databases , 2011, J. Comb. Optim..

[13]  Andy Schürr,et al.  Incremental Graph Pattern Matching , 2006 .

[14]  Vincenzo Bonnici,et al.  On the Variable Ordering in Subgraph Isomorphism Algorithms , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[15]  Chunming Hu,et al.  Big graph search: challenges and techniques , 2014, Frontiers of Computer Science.

[16]  Tinghuai Ma,et al.  Natural disaster topic extraction in Sina microblogging based on graph analysis , 2019, Expert Syst. Appl..

[17]  Dennis Shasha,et al.  GRAPES: A Software for Parallel Searching on Biological Graphs Targeting Multi-Core Architectures , 2013, PloS one.

[18]  Jian Shen,et al.  $$\varvec{\textit{KDVEM}}$$KDVEM: a $$k$$k-degree anonymity with vertex and edge modification algorithm , 2015, Computing.

[19]  Wilfred Ng,et al.  Efficient query processing on graph databases , 2009, TODS.

[20]  Guisheng Yin,et al.  An Incremental Graph Pattern Matching Based Dynamic Cold-Start Recommendation Method , 2016, ICYCSEE.

[21]  Philip S. Yu,et al.  Subgraph Matching with Set Similarity in a Large Graph Database , 2015, IEEE Transactions on Knowledge and Data Engineering.

[22]  Tinghuai Ma,et al.  A novel subgraph K+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K^{+}$$\end{document}-isomorphism method in social , 2017, Soft Computing.

[23]  Jeffrey Xu Yu,et al.  Exploring Triangle-Free Dense Structures , 2018, IEEE Transactions on Knowledge and Data Engineering.

[24]  Xin Wang,et al.  ExpFinder: Finding experts by graph pattern matching , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[25]  Tinghuai Ma,et al.  Detect structural‐connected communities based on BSCHEF in C‐DBLP , 2016, Concurr. Comput. Pract. Exp..

[26]  José Francisco Martínez Trinidad,et al.  Mining maximal frequent patterns in a single graph using inexact matching , 2014, Knowl. Based Syst..

[27]  Roded Sharan,et al.  Sigma: a Set-Cover-Based Inexact Graph Matching Algorithm , 2010, J. Bioinform. Comput. Biol..

[28]  Nan Li,et al.  Neighborhood based fast graph search in large networks , 2011, SIGMOD '11.

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

[30]  Charu C. Aggarwal,et al.  NeMa: Fast Graph Search with Label Similarity , 2013, Proc. VLDB Endow..

[31]  Sutanay Choudhury,et al.  Fast search for dynamic multi-relational graphs , 2013, DyNetMM '13.

[32]  Yao Wang,et al.  LED: A fast overlapping communities detection algorithm based on structural clustering , 2016, Neurocomputing.

[33]  Yongtang Shi,et al.  Fifty years of graph matching, network alignment and network comparison , 2016, Inf. Sci..

[34]  Yuan Tian,et al.  Protection of location privacy for moving kNN queries in social networks , 2017, Appl. Soft Comput..

[35]  Xiaodi Huang,et al.  On exploiting social relationship and personal background for content discovery in P2P networks , 2014, Future Gener. Comput. Syst..

[36]  Junhu Wang,et al.  Exploiting Vertex Relationships in Speeding up Subgraph Isomorphism over Large Graphs , 2015, Proc. VLDB Endow..

[37]  Mario Vento,et al.  A (sub)graph isomorphism algorithm for matching large graphs , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Tinghuai Ma,et al.  KDVEM : a k-degree anonymity with vertex and edge modification algorithm , 2015, Computing.

[39]  Anura P. Jayasumana,et al.  Investigative simulation: Towards utilizing graph pattern matching for investigative search , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[40]  Tinghuai Ma,et al.  Graph classification based on graph set reconstruction and graph kernel feature reduction , 2018, Neurocomputing.

[41]  Vesna Memisevic,et al.  Global G RAph A Lignment of Biological Networks , 2022 .

[42]  Natasa Przulj,et al.  L-GRAAL: Lagrangian graphlet-based network aligner , 2015, Bioinform..

[43]  Jiawei Han,et al.  On graph query optimization in large networks , 2010, Proc. VLDB Endow..

[44]  Lijun Chang,et al.  Efficient Subgraph Matching by Postponing Cartesian Products , 2016, SIGMOD Conference.

[45]  Peter J. McLane,et al.  Error bounds for trellis-coded MPSK on a fading mobile satellite channel , 1991, IEEE Trans. Commun..

[46]  Jeong-Hoon Lee,et al.  Turboiso: towards ultrafast and robust subgraph isomorphism search in large graph databases , 2013, SIGMOD '13.