Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Referral Web: combining social networks and collaborative filtering
Communications of the ACM
Mining generalized association rules
Future Generation Computer Systems - Special double issue on data mining
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering large dense subgraphs in massive graphs
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Assessing data mining results via swap randomization
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Coherent closed quasi-clique discovery from large dense graph databases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
Fast incremental proximity search in large graphs
Proceedings of the 25th international conference on Machine learning
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
An efficient rigorous approach for identifying statistically significant frequent itemsets
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
AdHeat: an influence-based diffusion model for propagating hints to match ads
Proceedings of the 19th international conference on World wide web
Randomization tests for distinguishing social influence and homophily effects
Proceedings of the 19th international conference on World wide web
Structural correlation pattern mining for large graphs
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Computer Science Review
Mining attribute-structure correlated patterns in large attributed graphs
Proceedings of the VLDB Endowment
User community reconstruction using sampled microblogging data
Proceedings of the 21st international conference companion on World Wide Web
Challenging the long tail recommendation
Proceedings of the VLDB Endowment
Measuring two-event structural correlations on graphs
Proceedings of the VLDB Endowment
Evaluating geo-social influence in location-based social networks
Proceedings of the 21st ACM international conference on Information and knowledge management
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Real-life graphs not only have nodes and edges, but also have events taking place, e.g., product sales in social networks and virus infection in communication networks. Among different events, some exhibit strong correlation with the network structure, while others do not. Such structural correlation will shed light on viral influence existing in the corresponding network. Unfortunately, the traditional association mining concept is not applicable in graphs since it only works on homogeneous datasets like transactions and baskets. We propose a novel measure for assessing such structural correlations in heterogeneous graph datasets with events. The measure applies hitting time to aggregate the proximity among nodes that have the same event. In order to calculate the correlation scores for many events in a large network, we develop a scalable framework, called gScore, using sampling and approximation. By comparing to the situation where events are randomly distributed in the same network, our method is able to discover events that are highly correlated with the graph structure. gScore is scalable and was successfully applied to the co-author DBLP network and social networks extracted from TaoBao.com, the largest online shopping network in China, with many interesting discoveries.