ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Support measures for graph data*
Data Mining and Knowledge Discovery
Pattern Mining in Frequent Dynamic Subgraphs
ICDM '06 Proceedings of the Sixth International Conference on 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
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Subgraph Support in a Single Large Graph
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Anti-monotonic Overlap-Graph Support Measures
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Spotting Significant Changing Subgraphs in Evolving Graphs
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A Fast Method to Mine Frequent Subsequences from Graph Sequence Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Monitoring Network Evolution using MDL
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
gPrune: a constraint pushing framework for graph pattern mining
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
What is frequent in a single graph?
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining temporally changing web usage graphs
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
A supervised machine learning link prediction approach for academic collaboration recommendation
Proceedings of the fourth ACM conference on Recommender systems
Mining interesting link formation rules in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
Network node label acquisition and tracking
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
REX: explaining relationships between entity pairs
Proceedings of the VLDB Endowment
GTRACE2: improving performance using labeled union graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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
Mining rules for rewriting states in a transition-based dependency parser
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
An efficiently computable support measure for frequent subgraph pattern mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Graph mining for object tracking in videos
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Nearly exact mining of frequent trees in large networks
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
LaFT-tree: perceiving the expansion trace of one's circle of friends in online social networks
Proceedings of the sixth ACM international conference on Web search and data mining
Discovering evolution chains in dynamic networks
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Mining discriminative subgraphs from global-state networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient algorithm for approximate betweenness centrality computation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
The web as an adaptive network: coevolution of web behavior and web structure
Proceedings of the 3rd International Web Science Conference
A modelling framework for social media monitoring
International Journal of Web Engineering and Technology
Evolving networks: Eras and turning points
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
Discovering descriptive rules in relational dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
Mining spatiotemporal patterns in dynamic plane graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
| Hi-index | 0.00 |
In this paper we introduce graph-evolution rules , a novel type of frequency-based pattern that describe the evolution of large networks over time, at a local level. Given a sequence of snapshots of an evolving graph, we aim at discovering rules describing the local changes occurring in it. Adopting a definition of support based on minimum image we study the problem of extracting patterns whose frequency is larger than a minimum support threshold. Then, similar to the classical association rules framework, we derive graph-evolution rules from frequent patterns that satisfy a given minimum confidence constraint. We discuss merits and limits of alternative definitions of support and confidence, justifying the chosen framework. To evaluate our approach we devise GERM (Graph Evolution Rule Miner), an algorithm to mine all graph-evolution rules whose support and confidence are greater than given thresholds. The algorithm is applied to analyze four large real-world networks (i.e., two social networks, and two co-authorship networks from bibliographic data), using different time granularities. Our extensive experimentation confirms the feasibility and utility of the presented approach. It further shows that different kinds of networks exhibit different evolution rules, suggesting the usage of these local patterns to globally discriminate different kind of networks.