Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
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The anatomy of a large-scale hypertextual Web search engine
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Connectivity and inference problems for temporal networks
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Keeping Up with the Changing Web
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SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SEQUEL: A structured English query language
SIGFIDET '74 Proceedings of the 1974 ACM SIGFIDET (now SIGMOD) workshop on Data description, access and control
Estimating frequency of change
ACM Transactions on Internet Technology (TOIT)
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
On the Bursty Evolution of Blogspace
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Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Modelling information persistence on the web
ICWE '06 Proceedings of the 6th international conference on Web engineering
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted graphs and disconnected components: patterns and a generator
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast mining of complex time-stamped events
Proceedings of the 17th ACM conference on Information and knowledge management
Temporal Evolution of the UK Web
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Co-evolution of social and affiliation networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the Temporal Dimension of the Information Propagation
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Structural inference of hierarchies in networks
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
As time goes by: discovering eras in evolving social networks
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Within the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure derived from the Jaccard coefficient between two temporal snapshots of the network, able to detect the turning points at the beginning of the eras. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks and null models, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset, a collaboration graph extracted from a cinema database, and a network extracted from a database of terrorist attacks; we illustrate how the discovered temporal clustering highlights the crucial moments when the networks witnessed profound changes in their structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.