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With the rapid growth of digital publishing, harvesting, managing, and analyzing scholarly information have become increasingly challenging. The term Big Scholarly Data is coined for the rapidly growing scholarly data, which contains information including millions of authors, papers, citations, figures, tables, as well as scholarly networks and digital libraries. Nowadays, various scholarly data can...
In recent years, the identification and application of strong and weak ties in social networks has drawn increasing attention. The traditional definitions of strong ties (with two endpoints in the same community) and weak ties (with two endpoints in different communities) are being challenged by overlapping community structures. Because of their unique nature, the relationships between overlapping...
Predicting microblog user retweet behaviors is the basis of building the information diffusion model in microblog social networks. In order to improve the accuracy of predicting user retweet behaviors, under the MRF (Markov Random Field) framework, the paper comprehensively analyzes the effects on user retweet behaviors caused by various features (e.g., user attributes and microblog contents) and...
This work proposes to investigate the question of whether attending conference will breed new scientific collaboration based on the focal closure theory. Through the analysis of conference closure on individual and community level, we show that attending conference can promote new scientific collaborations, and conferences with more attendees and higher field ratings bring more new scientific collaborations.
We introduce a social network analysis method as a new approach to build an Intrusion Detection System (SN-IDS) in ad hoc networks. The SN-IDS utilizes social relations as metrics-of-interest for anomaly detections, which is different from most traditional IDS approaches. To construct proper social networks, we first investigate ad hoc MAC and network layer data attributes and select relevant social...
Many networks, including social and biological networks, are naturally divided into communities. Community detection is an important task for the discovering underlying structure in networks. GN algorithm is one of the most influential detection algorithms based on betweenness scores of edges, but it is computationally costly, as all betweenness scores should be repeatedly computed once an edge is...
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