of multi-step relevance propagation in topicspecific expertise graphs. We model the process of expert finding by probabilistic random walks of three kinds: finite, infinite and absorbing. Experiments on TREC Enterprise Track data originating from two large organizations show that our methods using multi

? Is this approach better than mining message content alone? To find answers to these questions, two algorithms for determining expertise from email were compared: a contentbased approach that takes account only of email text, and a graph-based ranking algorithm (HITS) that takes account both of text

, we first collect pages that a user visits during a learning session, where the user browses the Web and specifically marks which pages she is interested in. We then examine the semantic content of these pages to construct a concept graph, which is used to learn the dominant content and link structure

We examine a cooperative activity of a sixth-grade class. The activity took place over 5 days and focused on inventing adequate static representations of motion. In generating, critiquing, and refining numerous representations, we find indications of strong meta-representational competence. In addition to conceptual and design competence, we focus on the structure of activities and find in them an intricate blend of (I) the children's conceptual and interactional skills, (2) their interest in, and sense of ownership over, the inventions, and (3) the teacher's initiation and organization of activities, which is deli-cately balanced with her letting the activities evolve according to student-set directions. 1.

. Contrast this with a ringshaped network, in which all actors are equally central. In the VC market, greater centrality may translate into better access to information, deal flow, deeper pools of capital, expertise, contacts, and so on. Network analysis uses a branch of mathematics called graph theory

with additional expertise is needed. In any case, tossing increases the time-to-correction for a bug. In this paper, we introduce a graph model based on Markov chains, which captures bug tossing history. This model has sev-eral desirable qualities. First, it reveals developer networks which can be used

operationalize contributor expertise and role. First, we revisit current expertise metrics and show that their use bundles many different aspects, creating ambiguity. Second, we introduce clearly-defined contributor roles, which capture multiple project facets. Third, we propose a graph model, based

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People search for people with suitable expertise all of the time in their social networks – to answer questions or provide help. Recently, efforts have been made to augment this searching. However, relatively little is known about the social characteristics of various algorithms that might

Recently expertise retrieval has received increasing interests in both academia and industry. Finding experts with demonstrated expertise for a given query is a nontrivial task especially from a large-scale Web 2.0 systems, such as question answering and bibliography data, where users are actively