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Efficient processing of streaming graphs for evolution-aware clustering
Published: 27 October 2013 Publication History
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
CIKM '13
Paper Acceptance Rate 143 of 848 submissions, 17% Overall Acceptance Rate 1,861 of 8,427 submissions, 22%
M. K. Agarwal, K. Ramamritham, and M. Bhide. Real time discovery of dense clusters in highly dynamic graphs: identifying real world events in highly dynamic environments. PVLDB, 5(10):980--991, 2012. Google Scholar Digital Library C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for clustering evolving data streams. In Proc. of the 29th Int. Conf. on Very Large Data Bases, 2003. Google Scholar Digital Library C. C. Aggarwal, Y. Zhao, and P. S. Yu. On clustering graph streams. In Proc. of the SIAM Int. Conf. on Data Mining, 2010. Google Scholar Cross Ref A. Angel, N. Koudas, N. Sarkas, and D. Srivastava. Dense subgraph maintenance under streaming edge weight updates for real-time story indentification. PVLDB, 5(6):574--585, 2012. Google Scholar Digital Library B. Bahmani, R. Kumar, and S. Vassilvitskii. Densest subgraph in streaming and MapReduce. PVLDB, 5(5):454--465, 2012. Google Scholar Digital Library D. Ediger, R. McColl, J. Riedy, and D. A. Bader. STINGER: High performance data structure for streaming graphs. In Proc. of IEEE High Performance Extreme Computing Conf., 2012. Google Scholar Cross Ref A. Eldawy, R. Khandekar, and K.-L. Wu. Clustering streaming graphs. In Proc. of the 32nd IEEE Int. Conf. on Distributed Computing Systems, 2012. Google Scholar Digital Library GraphInsight. http://www.graphinsight.com, 2013. Google Scholar M. Gupta, C. C. Aggarwal, J. Han, and Y. Sun. Evolutionary clustering and analysis of bibliographic networks. In Proc. of Int. Conf. on Advances in Social Networks Analysis and Mining, 2011. Google Scholar Digital Library M. Henzinger and V. King. Randomized fully dynamic graph algorithm with polylogarithmic time per operation. Journal of the ACM, 46(4):502--516, 1999. Google Scholar Digital Library IBM. InfoSphere Streams. http://www.ibm.com/software/data/infosphere/streams/. Google Scholar G. Karypis. Metis: a software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices. http://glaros.dtc.umn.edu/gkhome/views/metis, 2011. Google Scholar V. Kawadia and S. Sreenivasan. Online detection of temporal communities in evolving networks by estrangement confinement. Nature Scientific Reports, 2012. Google Scholar Y. Lin, Y. Chi, S. Zhu, H. Sundaram, and B. Tseng. Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In Proc. of the 17th Int. Conf. on World Wide Web, 2008. Google Scholar Digital Library I. Stanton and G. Kliot. Streaming graph partitioning for large distributed graphs. In Proc. of the 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2012. Google Scholar Digital Library
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The clustering of vertices often evolves with time in a streaming graph, where graph update events are given as a stream of edge (vertex) insertions and deletions. Although a sliding window in stream processing naturally captures some cluster evolution, it alone may not be adequate, especially if the window size is large and the clustering within the windowed stream is unstable. Prior graph clustering approaches are mostly insensitive to clustering evolution. In this paper, we present an efficient approach to processing streaming graphs for evolution-aware clustering (EAC) of vertices. We incrementally manage individual connected components as clusters subject to a constraint on the maximal cluster size. For each cluster, we keep the relative recency of edges in a sorted order and favor more recent edges in clustering. We evaluate the effectiveness of EAC and compare it with a previous state-of-the-art evolution-insensitive clustering (EIC) approach. The results show that EAC is both effective and efficient in capturing evolution in a streaming graph. Moreover, we implement EAC as a streaming graph operator on IBM's InfoSphere Streams, a large-scale distributed middleware for stream processing, and show snapshots of the user cluster evolution in a streaming Twitter mention graph.
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Efficient processing of streaming graphs for evolution-aware clustering
Keywords Clustering streaming graphs Evolution-aware clustering
ASJC Scopus subject areas Decision Sciences(all) Business, Management and Accounting(all)

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Yuan, M., Wu, K. L., Jacques-Silva, G. , & Lu, Y. (2013). Efficient processing of streaming graphs for evolution-aware clustering . In CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (pp. 319-328). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/2505515.2505750
Yuan, M, Wu, KL, Jacques-Silva, G & Lu, Y 2013, Efficient processing of streaming graphs for evolution-aware clustering . in CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, pp. 319-328, 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013, San Francisco, CA, United States, 10/27/13 . https://doi.org/10.1145/2505515.2505750
Yuan M, Wu KL, Jacques-Silva G , Lu Y . Efficient processing of streaming graphs for evolution-aware clustering . In CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. 2013. p. 319-328. (International Conference on Information and Knowledge Management, Proceedings). doi: 10.1145/2505515.2505750
Yuan, Mindi ; Wu, Kun Lung ; Jacques-Silva, Gabriela et al. / Efficient processing of streaming graphs for evolution-aware clustering . CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. 2013. pp. 319-328 (International Conference on Information and Knowledge Management, Proceedings).
@inproceedings{0ef7630ea60a46f39a636d71a78de36a,
title = "Efficient processing of streaming graphs for evolution-aware clustering",
abstract = "The clustering of vertices often evolves with time in a streaming graph, where graph update events are given as a stream of edge (vertex) insertions and deletions. Although a sliding window in stream processing naturally captures some cluster evolution, it alone may not be adequate, especially if the window size is large and the clustering within the windowed stream is unstable. Prior graph clustering approaches are mostly insensitive to clustering evolution. In this paper, we present an efficient approach to processing streaming graphs for evolution-aware clustering (EAC) of vertices. We incrementally manage individual connected components as clusters subject to a constraint on the maximal cluster size. For each cluster, we keep the relative recency of edges in a sorted order and favor more recent edges in clustering. We evaluate the effectiveness of EAC and compare it with a previous state-of-the-art evolution-insensitive clustering (EIC) approach. The results show that EAC is both effective and efficient in capturing evolution in a streaming graph. Moreover, we implement EAC as a streaming graph operator on IBM's InfoSphere Streams, a large-scale distributed middleware for stream processing, and show snapshots of the user cluster evolution in a streaming Twitter mention graph.",
keywords = "Clustering streaming graphs, Evolution-aware clustering",
author = "Mindi Yuan and Wu, {Kun Lung} and Gabriela Jacques-Silva and Yi Lu",
doi = "10.1145/2505515.2505750",
series = "International Conference on Information and Knowledge Management, Proceedings",
booktitle = "CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management",
note = "22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 ; Conference date: 27-10-2013 Through 01-11-2013",
Cookies Settings Accept All Cookies
Mindi Yuan, Kun Lung Wu, Gabriela Jacques-Silva, Yi Lu
Research output : Chapter in Book/Report/Conference proceeding › Conference contribution
The clustering of vertices often evolves with time in a streaming graph, where graph update events are given as a stream of edge (vertex) insertions and deletions. Although a sliding window in stream processing naturally captures some cluster evolution, it alone may not be adequate, especially if the window size is large and the clustering within the windowed stream is unstable. Prior graph clustering approaches are mostly insensitive to clustering evolution. In this paper, we present an efficient approach to processing streaming graphs for evolution-aware clustering (EAC) of vertices. We incrementally manage individual connected components as clusters subject to a constraint on the maximal cluster size. For each cluster, we keep the relative recency of edges in a sorted order and favor more recent edges in clustering. We evaluate the effectiveness of EAC and compare it with a previous state-of-the-art evolution-insensitive clustering (EIC) approach. The results show that EAC is both effective and efficient in capturing evolution in a streaming graph. Moreover, we implement EAC as a streaming graph operator on IBM's InfoSphere Streams, a large-scale distributed middleware for stream processing, and show snapshots of the user cluster evolution in a streaming Twitter mention graph.
Efficient processing of streaming graphs for evolution-aware clustering. / Yuan, Mindi; Wu, Kun Lung; Jacques-Silva, Gabriela et al.
Research output : Chapter in Book/Report/Conference proceeding › Conference contribution
T1 - Efficient processing of streaming graphs for evolution-aware clustering
N2 - The clustering of vertices often evolves with time in a streaming graph, where graph update events are given as a stream of edge (vertex) insertions and deletions. Although a sliding window in stream processing naturally captures some cluster evolution, it alone may not be adequate, especially if the window size is large and the clustering within the windowed stream is unstable. Prior graph clustering approaches are mostly insensitive to clustering evolution. In this paper, we present an efficient approach to processing streaming graphs for evolution-aware clustering (EAC) of vertices. We incrementally manage individual connected components as clusters subject to a constraint on the maximal cluster size. For each cluster, we keep the relative recency of edges in a sorted order and favor more recent edges in clustering. We evaluate the effectiveness of EAC and compare it with a previous state-of-the-art evolution-insensitive clustering (EIC) approach. The results show that EAC is both effective and efficient in capturing evolution in a streaming graph. Moreover, we implement EAC as a streaming graph operator on IBM's InfoSphere Streams, a large-scale distributed middleware for stream processing, and show snapshots of the user cluster evolution in a streaming Twitter mention graph.
AB - The clustering of vertices often evolves with time in a streaming graph, where graph update events are given as a stream of edge (vertex) insertions and deletions. Although a sliding window in stream processing naturally captures some cluster evolution, it alone may not be adequate, especially if the window size is large and the clustering within the windowed stream is unstable. Prior graph clustering approaches are mostly insensitive to clustering evolution. In this paper, we present an efficient approach to processing streaming graphs for evolution-aware clustering (EAC) of vertices. We incrementally manage individual connected components as clusters subject to a constraint on the maximal cluster size. For each cluster, we keep the relative recency of edges in a sorted order and favor more recent edges in clustering. We evaluate the effectiveness of EAC and compare it with a previous state-of-the-art evolution-insensitive clustering (EIC) approach. The results show that EAC is both effective and efficient in capturing evolution in a streaming graph. Moreover, we implement EAC as a streaming graph operator on IBM's InfoSphere Streams, a large-scale distributed middleware for stream processing, and show snapshots of the user cluster evolution in a streaming Twitter mention graph.
UR - http://www.scopus.com/inward/record.url?scp=84889584979&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84889584979&partnerID=8YFLogxK
T3 - International Conference on Information and Knowledge Management, Proceedings
BT - CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
T2 - 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
Y2 - 27 October 2013 through 1 November 2013

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CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
International Conference on Information and Knowledge Management, Proceedings
22nd ACM International Conference on Information and Knowledge Management, CIKM 2013

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