The Structure of Heterogeneous Networks Export

@inproceedings{citeulike:4817156, abstract = {Heterogeneous networks play a key role in the evolution of communities and the decisions individuals make. These networks link different types of entities, for example, people and the events they attend. Network analysis algorithms usually project such networks unto simple graphs composed of entities of a single type. In the process, they conflate relations between entities of different types and loose important structural information. We develop a mathematical framework that can be used to compactly represent and analyze heterogeneous networks that combine multiple entity and link types. We generalize Bonacich centrality, which measures connectivity between nodes by the number of paths between them, to heterogeneous networks and use this measure to study network structure. Specifically, we extend the popular modularity-maximization method for community detection to use this centrality metric. We also rank nodes based on their connectivity to other nodes. One advantage of this centrality metric is that it has a tunable parameter we can use to set the length scale of interactions. By studying how rankings change with this parameter allows us to identify important nodes in the network. We apply the proposed method to analyze the structure of several heterogeneous networks. We show that exploiting additional sources of evidence corresponding to links between, as well as among, different entity types yields new insights into network structure.}, archivePrefix = {arXiv}, author = {Ghosh, Rumi and Lerman, Kristina}, booktitle = {Proc. of the 1st IEEE Social Computing Conference}, citeulike-article-id = {4817156}, citeulike-linkout-0 = {http://arxiv.org/abs/0906.2212}, eprint = {0906.2212}, keywords = {centrality, community-detection, community-finding, groups, heterogeneous, influence, lerman, metrix, modularity, mynetworks, mysocialweb, networks, sna, socialnetworks, u24}, posted-at = {2009-06-11 23:10:41}, priority = {2}, title = {The Structure of Heterogeneous Networks}, url = {http://arxiv.org/abs/0906.2212}, year = {2009} }

TY - CONF ID - citeulike:4817156 L3 - citeulike-article-id:4817156 N2 - Heterogeneous networks play a key role in the evolution of communities and the decisions individuals make. These networks link different types of entities, for example, people and the events they attend. Network analysis algorithms usually project such networks unto simple graphs composed of entities of a single type. In the process, they conflate relations between entities of different types and loose important structural information. We develop a mathematical framework that can be used to compactly represent and analyze heterogeneous networks that combine multiple entity and link types. We generalize Bonacich centrality, which measures connectivity between nodes by the number of paths between them, to heterogeneous networks and use this measure to study network structure. Specifically, we extend the popular modularity-maximization method for community detection to use this centrality metric. We also rank nodes based on their connectivity to other nodes. One advantage of this centrality metric is that it has a tunable parameter we can use to set the length scale of interactions. By studying how rankings change with this parameter allows us to identify important nodes in the network. We apply the proposed method to analyze the structure of several heterogeneous networks. We show that exploiting additional sources of evidence corresponding to links between, as well as among, different entity types yields new insights into network structure. TI - The Structure of Heterogeneous Networks T2 - Proc. of the 1st IEEE Social Computing Conference KW - centrality KW - community-detection KW - community-finding KW - groups KW - heterogeneous KW - influence KW - lerman KW - metrix KW - modularity KW - mynetworks KW - mysocialweb KW - networks KW - sna KW - socialnetworks KW - u24 AU - Ghosh, Rumi AU - Lerman, Kristina PY - 2009/// UR - http://arxiv.org/abs/0906.2212 ER -