Similarity Analysis of Learning Interests among Majors Using Complex Networks At present, multispecialization cross integration is the new trend for highlevel personnel training and scientific and technological innovation. A similarity analysis of learning interests among specializations based on book borrowing behavior is proposed in this paper. Students of different majors that borrow the same book can be regarded as a way of measuring similar learning interests among majors. Considering the borrowing data of 75 majors, 14,600 undergraduates, and 280,000 books at the Northwest Normal University (NWNU), as an example, this study classified readers into majors depending on similarity among students. A complex network of similar learning interests among specializations was constructed using group behavior data. The characteristics of learning interests were revealed among majors through a network topology analysis, importance of network nodes, and calculation of the similarity among different majors by the Louvain algorithm. The study concluded that the major cooccurrence network was characterized as scalefree and smallworld; most majors had mutual communication and an infiltrating relationship, and the 75 majors of NWNU may form six major interest groups. The conclusions of the study were related to the development of majors of the university, and a match between major learning communities was based on the borrowing interest in a similar network to reflect the relationship between the characteristics and internal operating rules of a major.

A Concise Social Network Representation with Flow Hierarchy Using Frequent Interactions In this paper we introduce the flow hierarchy derived from frequent interactions to represent complex social networks in a concise way. Frequent interactions extract the impactful users while the flow hierarchy visualizes the dependencies and identifies different roles such as leaders, topic experts, information disseminators, emerging leaders and active followers. It is highly applicable in intelligent systems which involve user ranking, expert searching, recommendation, viral marketing, political campaigning, disaster management and many more. We present novel methods of deriving flow hierarchy considering the temporal dimension of interactions among users, flow directions and structural dependencies. We empirically evaluate proposed methods using realworld social network interaction datasets related to citation and retweet networks. Empirical analysis reveals that a hierarchy derived from frequent social interactions is effective in extracting the impactful users and their position in the network. Baseline results with usercentric measures show the efficacy of the proposed methods in finding a concise network representation.