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2018 | OriginalPaper | Buchkapitel

1. Introduction

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Abstract

With the rapid development of information technology such as social media, online communities, and mobile communications, huge volumes of digital data are accumulated with data entities involving complex relationships. These data are usually modelled as graphs in view of the simple yet strong expressive power of graph model; that is, entities are represented by vertices and relationships are represented by edges.

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Literatur
1.
Zurück zum Zitat James Abello, Mauricio G. C. Resende, and Sandra Sudarsky. Massive quasi-clique detection. In Proc. of LATIN’02, pages 598–612, 2002. James Abello, Mauricio G. C. Resende, and Sandra Sudarsky. Massive quasi-clique detection. In Proc. of LATIN’02, pages 598–612, 2002.
3.
Zurück zum Zitat Takuya Akiba, Yoichi Iwata, and Yuichi Yoshida. Linear-time enumeration of maximal k-edge-connected subgraphs in large networks by random contraction. In Proc. CIKM’13, pages 909–918, 2013. Takuya Akiba, Yoichi Iwata, and Yuichi Yoshida. Linear-time enumeration of maximal k-edge-connected subgraphs in large networks by random contraction. In Proc. CIKM’13, pages 909–918, 2013.
4.
Zurück zum Zitat Albert Angel, Nick Koudas, Nikos Sarkas, and Divesh Srivastava. Dense subgraph maintenance under streaming edge weight updates for real-time story identification. PVLDB, 5(6):574–585, 2012. Albert Angel, Nick Koudas, Nikos Sarkas, and Divesh Srivastava. Dense subgraph maintenance under streaming edge weight updates for real-time story identification. PVLDB, 5(6):574–585, 2012.
10.
Zurück zum Zitat Fei Bi, Lijun Chang, Xuemin Lin, and Wenjie Zhang. An optimal and progressive approach to online search of top-k influential communities. PVLDB, 11(9):1056–1068, 2018. Fei Bi, Lijun Chang, Xuemin Lin, and Wenjie Zhang. An optimal and progressive approach to online search of top-k influential communities. PVLDB, 11(9):1056–1068, 2018.
11.
Zurück zum Zitat S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang. Complex networks: Structure and dynamics. Physics Reports, 424(4–5):175–308, 2006.MathSciNetCrossRef S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang. Complex networks: Structure and dynamics. Physics Reports, 424(4–5):175–308, 2006.MathSciNetCrossRef
12.
Zurück zum Zitat Paolo Boldi and Sebastiano Vigna. The WebGraph framework I: Compression techniques. In Proc. of WWW’04, pages 595–601, 2004. Paolo Boldi and Sebastiano Vigna. The WebGraph framework I: Compression techniques. In Proc. of WWW’04, pages 595–601, 2004.
16.
Zurück zum Zitat Lijun Chang, Xuemin Lin, Lu Qin, Jeffrey Xu Yu, and Wenjie Zhang. Index-based optimal algorithms for computing Steiner components with maximum connectivity. In Proc. of SIGMOD’15, 2015. Lijun Chang, Xuemin Lin, Lu Qin, Jeffrey Xu Yu, and Wenjie Zhang. Index-based optimal algorithms for computing Steiner components with maximum connectivity. In Proc. of SIGMOD’15, 2015.
17.
Zurück zum Zitat Lijun Chang, Jeffrey Xu Yu, Lu Qin, Xuemin Lin, Chengfei Liu, and Weifa Liang. Efficiently computing k-edge connected components via graph decomposition. In Proc. SIGMOD’13, pages 205–216, 2013. Lijun Chang, Jeffrey Xu Yu, Lu Qin, Xuemin Lin, Chengfei Liu, and Weifa Liang. Efficiently computing k-edge connected components via graph decomposition. In Proc. SIGMOD’13, pages 205–216, 2013.
18.
Zurück zum Zitat Moses Charikar. Greedy approximation algorithms for finding dense components in a graph. In Proc. APPROX’00, pages 84–95, 2000. Moses Charikar. Greedy approximation algorithms for finding dense components in a graph. In Proc. APPROX’00, pages 84–95, 2000.
22.
Zurück zum Zitat Jonathan Cohen. Trusses: Cohesive subgraphs for social network analysis, 2008. Jonathan Cohen. Trusses: Cohesive subgraphs for social network analysis, 2008.
23.
Zurück zum Zitat Alessio Conte, Donatella Firmani, Caterina Mordente, Maurizio Patrignani, and Riccardo Torlone. Fast enumeration of large k-plexes. In Proc. of KDD’17, pages 115–124, 2017. Alessio Conte, Donatella Firmani, Caterina Mordente, Maurizio Patrignani, and Riccardo Torlone. Fast enumeration of large k-plexes. In Proc. of KDD’17, pages 115–124, 2017.
24.
Zurück zum Zitat Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms (3. ed.). MIT Press, 2009. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms (3. ed.). MIT Press, 2009.
33.
Zurück zum Zitat David Gibson, Ravi Kumar, and Andrew Tomkins. Discovering large dense subgraphs in massive graphs. In Proc. of VLDB’05, pages 721–732, 2005. David Gibson, Ravi Kumar, and Andrew Tomkins. Discovering large dense subgraphs in massive graphs. In Proc. of VLDB’05, pages 721–732, 2005.
35.
Zurück zum Zitat A. V. Goldberg. Finding a maximum density subgraph. Technical report, Berkeley, CA, USA, 1984. A. V. Goldberg. Finding a maximum density subgraph. Technical report, Berkeley, CA, USA, 1984.
41.
Zurück zum Zitat Xin Huang, Hong Cheng, Lu Qin, Wentao Tian, and Jeffrey Xu Yu. Querying k-truss community in large and dynamic graphs. In Proc. of SIGMOD’14, pages 1311–1322, 2014. Xin Huang, Hong Cheng, Lu Qin, Wentao Tian, and Jeffrey Xu Yu. Querying k-truss community in large and dynamic graphs. In Proc. of SIGMOD’14, pages 1311–1322, 2014.
42.
Zurück zum Zitat Xin Huang, Laks V. S. Lakshmanan, and Jianliang Xu. Community search over big graphs: Models, algorithms, and opportunities. In Proc. of ICDE’17, pages 1451–1454, 2017. Xin Huang, Laks V. S. Lakshmanan, and Jianliang Xu. Community search over big graphs: Models, algorithms, and opportunities. In Proc. of ICDE’17, pages 1451–1454, 2017.
46.
Zurück zum Zitat M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. E. Stanley, and H. A. Makse. Identification of influential spreaders in complex networks. Nature Physics, 6:888–893, 2010.CrossRef M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. E. Stanley, and H. A. Makse. Identification of influential spreaders in complex networks. Nature Physics, 6:888–893, 2010.CrossRef
48.
Zurück zum Zitat Victor E. Lee, Ning Ruan, Ruoming Jin, and Charu C. Aggarwal. A survey of algorithms for dense subgraph discovery. In Managing and Mining Graph Data, pages 303–336. 2010. Victor E. Lee, Ning Ruan, Ruoming Jin, and Charu C. Aggarwal. A survey of algorithms for dense subgraph discovery. In Managing and Mining Graph Data, pages 303–336. 2010.
55.
Zurück zum Zitat F. D. Malliaros, M.-E. G. Rossi, and M. Vazirgiannis. Locating influential nodes in complex networks. Scientific Reports, 6, 2016. F. D. Malliaros, M.-E. G. Rossi, and M. Vazirgiannis. Locating influential nodes in complex networks. Scientific Reports, 6, 2016.
57.
Zurück zum Zitat Fragkiskos D. Malliaros and Michalis Vazirgiannis. Graph-based text representations: Boosting text mining, nlp and information retrieval with graphs. In Proc. of EMNLP’17, 2017. Fragkiskos D. Malliaros and Michalis Vazirgiannis. Graph-based text representations: Boosting text mining, nlp and information retrieval with graphs. In Proc. of EMNLP’17, 2017.
59.
Zurück zum Zitat David W. Matula and Leland L. Beck. Smallest-last ordering and clustering and graph coloring algorithms. J. ACM, 30(3):417–427, 1983.MathSciNetCrossRef David W. Matula and Leland L. Beck. Smallest-last ordering and clustering and graph coloring algorithms. J. ACM, 30(3):417–427, 1983.MathSciNetCrossRef
71.
Zurück zum Zitat Ryan A. Rossi and Nesreen K. Ahmed. The network data repository with interactive graph analytics and visualization. In Proc. of AAAI’15, 2015. Ryan A. Rossi and Nesreen K. Ahmed. The network data repository with interactive graph analytics and visualization. In Proc. of AAAI’15, 2015.
72.
Zurück zum Zitat François Rousseau and Michalis Vazirgiannis. Main core retention on graph-of-words for single-document keyword extraction. In Proc. of ECIR’15, pages 382–393, 2015. François Rousseau and Michalis Vazirgiannis. Main core retention on graph-of-words for single-document keyword extraction. In Proc. of ECIR’15, pages 382–393, 2015.
73.
Zurück zum Zitat Yousef Saad. Iterative methods for sparse linear systems. SIAM, 2003.CrossRef Yousef Saad. Iterative methods for sparse linear systems. SIAM, 2003.CrossRef
76.
Zurück zum Zitat Ahmet Erdem Sariyüce and Ali Pinar. Fast hierarchy construction for dense subgraphs. PVLDB, 10(3):97–108, 2016. Ahmet Erdem Sariyüce and Ali Pinar. Fast hierarchy construction for dense subgraphs. PVLDB, 10(3):97–108, 2016.
80.
Zurück zum Zitat Pablo San Segundo, Alvaro Lopez, and Panos M. Pardalos. A new exact maximum clique algorithm for large and massive sparse graphs. Computers & Operations Research, 66:81–94, 2016.MathSciNetCrossRef Pablo San Segundo, Alvaro Lopez, and Panos M. Pardalos. A new exact maximum clique algorithm for large and massive sparse graphs. Computers & Operations Research, 66:81–94, 2016.MathSciNetCrossRef
81.
82.
Zurück zum Zitat Stephen B. Seidman and Brian L. Foster. A graph-theoretic generalization of the clique concept. The Journal of Mathematical Sociology, 6(1):139–154, 1978.MathSciNetCrossRef Stephen B. Seidman and Brian L. Foster. A graph-theoretic generalization of the clique concept. The Journal of Mathematical Sociology, 6(1):139–154, 1978.MathSciNetCrossRef
84.
Zurück zum Zitat Mauro Sozio and Aristides Gionis. The community-search problem and how to plan a successful cocktail party. In Proc. of KDD’10, pages 939–948, 2010. Mauro Sozio and Aristides Gionis. The community-search problem and how to plan a successful cocktail party. In Proc. of KDD’10, pages 939–948, 2010.
88.
Zurück zum Zitat Antoine J.-P. Tixier, Fragkiskos D. Malliaros, and Michalis Vazirgiannis. A graph degeneracy-based approach to keyword extraction. In Proc. of EMNLP’16, pages 1860–1870, 2016. Antoine J.-P. Tixier, Fragkiskos D. Malliaros, and Michalis Vazirgiannis. A graph degeneracy-based approach to keyword extraction. In Proc. of EMNLP’16, pages 1860–1870, 2016.
89.
Zurück zum Zitat Charalampos E. Tsourakakis. The k-clique densest subgraph problem. In Proc. of WWW’15, pages 1122–1132, 2015. Charalampos E. Tsourakakis. The k-clique densest subgraph problem. In Proc. of WWW’15, pages 1122–1132, 2015.
91.
Zurück zum Zitat Michalis Vazirgiannis. Graph of words: Boosting text mining tasks with graphs. In Proc. of WWW’17, page 1181, 2017. Michalis Vazirgiannis. Graph of words: Boosting text mining tasks with graphs. In Proc. of WWW’17, page 1181, 2017.
92.
Zurück zum Zitat Jia Wang and James Cheng. Truss decomposition in massive networks. PVLDB, 5(9):812–823, 2012. Jia Wang and James Cheng. Truss decomposition in massive networks. PVLDB, 5(9):812–823, 2012.
93.
Zurück zum Zitat Jim Webber. The top 5 use cases of graph databases (white paper), 2015. Jim Webber. The top 5 use cases of graph databases (white paper), 2015.
99.
Zurück zum Zitat Long Yuan, Lu Qin, Xuemin Lin, Lijun Chang, and Wenjie Zhang. I/O efficient ECC graph decomposition via graph reduction. PVLDB, 9(7):516–527, 2016. Long Yuan, Lu Qin, Xuemin Lin, Lijun Chang, and Wenjie Zhang. I/O efficient ECC graph decomposition via graph reduction. PVLDB, 9(7):516–527, 2016.
102.
Zurück zum Zitat Rui Zhou, Chengfei Liu, Jeffrey Xu Yu, Weifa Liang, Baichen Chen, and Jianxin Li. Finding maximal k-edge-connected subgraphs from a large graph. In Proc. of EDBT’12, 2012. Rui Zhou, Chengfei Liu, Jeffrey Xu Yu, Weifa Liang, Baichen Chen, and Jianxin Li. Finding maximal k-edge-connected subgraphs from a large graph. In Proc. of EDBT’12, 2012.
Metadaten
Titel
Introduction
verfasst von
Lijun Chang
Lu Qin
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-03599-0_1