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A social network is made up of nodes and ties (Wasserman and Faust, 1994). The nodes can be individuals, teams, or organizations, while ties represent interper - sonal or interorganizational relationships, like trust, friendships, and investments. Among various scientific research questions related to social network dynamics, one of the most crucial is how people build, maintain, or change ties between them. There exist numerous works in the literature to answer this question. For example, Katz et al. (2004) listed in their review the following five schools of theories that attempt to answer that question: theories of self-interest, theories of social exchange or dependency, theories of mutual or collective interest, cognitive theories, and the - ories of homophily. The cognitive theories that we focus on in this chapter discuss how people’s cognitions of certain relationships, other people, objects, or processes influence their relationship building. By asking how much knowledge sharing is enough for building interdisciplinary collaborations (ICs), the goal of this chapter is to identify the cognitive-based reasons for people from various disciplines to share enough information to build their collaboration ties.

As scientists increasingly reach across disciplinary divides, the success of collaborations can hinge on how they choose to exchange knowledge. Our analysis suggests that their approach should be based on an understanding of the way that different teams initiate research and the subjects they choose to cover. We offer some practical tips for researchers to improve the effectiveness of their interdisciplinary collaborations.

Investigating online social behaviors may help us to better understand and predict offline high risk behaviors in gay communities. But how can offline behaviors be predicted from online social networks? This article selects data from 26 online social network groups from QQ (a Chinese based messaging software) administered by gay communities of "W" city of Hubei Province, China. Based on online data mining, social network analysis, and offline semi-structural interviews, we argue that the ego-centric dynamical network analysis---an approach which combines partial network dynamics, individual features, and structure position together---can be used to derive the probabilistic features for predicting offline high risk behaviors (HRB). An example of HRB is "one night stands" (gays for one night: 419) for gay homosexuals.

https://www.natureindex.com/news-blog/what- are-fake-interdisciplinary-collaborations-and-why-do-they-occur