Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Random walk with restart: fast solutions and applications
Knowledge and Information Systems
Introduction to Information Retrieval
Introduction to Information Retrieval
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable learning of collective behavior based on sparse social dimensions
Proceedings of the 18th ACM conference on Information and knowledge management
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
The Sum-over-Paths Covariance Kernel: A Novel Covariance Measure between Nodes of a Directed Graph
IEEE Transactions on Pattern Analysis and Machine Intelligence
Networks: An Introduction
Design patterns for efficient graph algorithms in MapReduce
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Twister: a runtime for iterative MapReduce
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Community detection using a measure of global influence
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
A multi-resolution approach to learning with overlapping communities
Proceedings of the First Workshop on Social Media Analytics
Text categorization with class-based and corpus-based keyword selection
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Scaling up Machine Learning: Parallel and Distributed Approaches
Scaling up Machine Learning: Parallel and Distributed Approaches
Defining and evaluating network communities based on ground-truth
Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
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Although criticized for some of its limitations, modularity remains a standard measure for analyzing social networks. Quantifying the statistical surprise in the arrangement of the edges of the network has led to simple and powerful algorithms. However, relying solely on the distribution of edges instead of more complex structures such as paths limits the extent of modularity. Indeed, recent studies have shown restrictions of optimizing modularity, for instance its resolution limit. We introduce here a novel, formal and well-defined modularity measure based on random walks. We show how this modularity can be computed from paths induced by the graph instead of the traditionally used edges. We argue that by computing modularity on paths instead of edges, more informative features can be extracted from the network. We verify this hypothesis on a semi-supervised classification procedure of the nodes in the network, where we show that, under the same settings, the features of the random walk modularity help to classify better than the features of the usual modularity. Additionally, the proposed approach outperforms the classical label propagation procedure on two data sets of labeled social networks.