We propose and study a set of algorithms for discovering community structure in networksnatural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.

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Clients with generalized anxiety disorder (GAD) received either (a) applied relaxation and self-control desensitization, (b) cognitive therapy, or (c) a combination of these methods. Treatment resulted in significant improvement in anxiety and depression that was maintained for 2 years. The large majority no longer met diagnostic criteria; a minority sought further treatment during follow-up. No differences in outcome were found between conditions; review of the GAD therapy literature suggested that this may have been due to strong effects generated by each component condition. Finally, interpersonal difficulties remaining at posttherapy, measured by the Inventory of Interpersonal Problems Circumplex Scales (L. E. Alden, J. S. Wiggins, & A. L. Pincus, 1990) in a subset of clients, were negatively associated with posttherapy and follow-up improvement, suggesting the possible utility of adding interpersonal treatment to cognitive-behavioral therapy to increase therapeutic effectiveness.