Understanding how topics in scientific literature evolve is an interesting and important problem. Previous work simply models each paper as a bag of words and also considers the impact of authors. However, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. In this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. We propose an iterative topic evolution learning framework by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model. We evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by CiteSeerX. The results clearly show that citations can help to understand topic evolution better.

Community discovery has drawn significant re-inspired by the success of the application of LDA(Latent search interests among researchers from many disciplines for Dirichlet Allocation) models in the information retrieval and its increasing application in multiple, disparate areas, including image analysis domains. In this model, communities are computer science, biology, social science and so on. This paper .. 'describes an LDA(latent Dirichlet Allocation)-based hierarchical modeled as latent variahles and are considered as distrilutions Bayesian algorithm, namely SSN-LDA(Simple Social Network on the entire social actor space. This way each social actor LDA). In SSN-LDA, communities are modeled as latent variables contributes a part, big or small, to every community in the in the graphical model and defined as distributions over the social society. We also propose three different approaches to create actor space. The advantage of SSN-LDA is that it only requires social interaction profiles hased on the social interaction infortopological information as input. This model is evaluated on two research collaborative networks:CiteSeer and NanoSCI. The mation in the network. The latent probabilistic model and three experimental results demonstrate that this approach is promising pertaining representation approaches are evaluated on two cofor discovering community structures in large-scale networks.' authorship networks from two distinct academic communities, i.e Na;noSCI from the nanotechnology domain and CiteSeer

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Using the dataset from a popular OHC, the research demonstrated that the proposed metric is highly effective in identifying influential users. In addition, combining the metric with other traditional measures further improves the identification of influential users.

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The rapid advancement of nanotechnology research and development during the past decade presents an excellent opportunity for a scientometric study because it can provide insights into the dynamic growth of the fastevolving social networks associated with this field. In this article, we describe a case study conducted on nanotechnology to discover the dynamics that govern the growth process of rapidly advancing scientific-collaboration networks. This article starts with the definition of temporal social networks and demonstrates that the nanotechnology collaboration network, similar to other real-world social networks, exhibits a set of intriguing static and dynamic topological properties. Inspired by the observations that in collaboration networks new connections tend to be augmented between nodes in proximity, we explore the locality elements and the attachedness factor in growing networks. In particular, we develop two distance-based computational network growth schemes, namely the distance-based growth model (DG) and the hybrid degree and distance-based growth model (DDG). The DG model considers only locality element while the DDG is a hybrid model that factors into both locality and attachedness elements. The simulation results from these models indicate that both clustering coefficient rates and the average shortest distance are closely related to the edge densification rates. In addition, the hybrid DDG model exhibits higher clustering coefficient values and decreasing average shortest distance when the edge densification rate is fixed, which implies that combining locality and attachedness can better characterize the growing process of the nanotechnology community. Based on the simulation results, we conclude that social network evolution is related to both attachedness and locality factors.

Abstract:In many social networks, the connections between actors are formed because they participate in the same event, such as a set of scholars co-authoring a paper or colleagues having a teleconference. Therefore, we propose an event-driven model to capture the growth dynamics of social networks through modelling of the social events. We also investigate the evolution of event formation and the joint effect of attachedness and locality on the selection of participants for events in real social networks. We incorporate the evolution of event formation and the joint effect of attachedness and locality into our model. The experimental results suggest that our approach can simulate important network structures, such as hierarchical communities and assortativity, and better characterise the growing process of real networks than non-event driven models.Keywords: social network analysis; social network modelling; behaviour evolution; event-driven; simulation; collaborative networks; power-law degree distributions; clustering coefficients; assortative mixing.Reference to this paper should be made as follows: Qiu, B., Ivanova, K., Yen, J., Liu, P. and Ritter, F.E. (2011)