Discovering shared interests using graph analysis
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A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Computational & Mathematical Organization Theory
Structure and evolution of online social networks
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Connectivity structure of bipartite graphs via the KNC-plot
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Planetary-scale views on a large instant-messaging network
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Characterizing individual communication patterns
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Suggesting friends using the implicit social graph
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Predicting product adoption in large-scale social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Self-adjusting hybrid recommenders based on social network analysis
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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An analysis of the structure and dynamics of large-scale Q/A communities
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Extracting semantic user networks from informal communication exchanges
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Predicting tie strength in a new medium
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Echoes of power: language effects and power differences in social interaction
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Regional subgraph discovery in social networks
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Building a dynamic and computational understanding of personal social networks
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Clustering social networks using interaction semantics and sentics
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Temporal semantic centrality for the analysis of communication networks
ICWE'12 Proceedings of the 12th international conference on Web Engineering
WebTribe: dynamic community analysis from online forums
ICWE'12 Proceedings of the 12th international conference on Web Engineering
Beyond Social Graphs: User Interactions in Online Social Networks and their Implications
ACM Transactions on the Web (TWEB)
From face-to-face gathering to social structure
Proceedings of the 21st ACM international conference on Information and knowledge management
Friends FTW! friendship and competition in halo: reach
Proceedings of the 2013 conference on Computer supported cooperative work
Uncovering the wider structure of extreme right communities spanning popular online networks
Proceedings of the 5th Annual ACM Web Science Conference
Inferring social roles and statuses in social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Alternate views of graph clusterings based on thresholds: a case study for a student forum
Proceedings of the sixth workshop on Ph.D. students in information and knowledge management
Computational perspectives on social phenomena at global scales
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Researchers increasingly use electronic communication data to construct and study large social networks, effectively inferring unobserved ties (e.g. i is connected to j) from observed communication events (e.g. i emails j). Often overlooked, however, is the impact of tie definition on the corresponding network, and in turn the relevance of the inferred network to the research question of interest. Here we study the problem of network inference and relevance for two email data sets of different size and origin. In each case, we generate a family of networks parameterized by a threshold condition on the frequency of emails exchanged between pairs of individuals. After demonstrating that different choices of the threshold correspond to dramatically different network structures, we then formulate the relevance of these networks in terms of a series of prediction tasks that depend on various network features. In general, we find: a) that prediction accuracy is maximized over a non-trivial range of thresholds corresponding to 5-10 reciprocated emails per year; b) that for any prediction task, choosing the optimal value of the threshold yields a sizable (~30%) boost in accuracy over naive choices; and c) that the optimal threshold value appears to be (somewhat surprisingly) consistent across data sets and prediction tasks. We emphasize the practical utility in defining ties via their relevance to the prediction task(s) at hand and discuss implications of our empirical results.