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Objective Online health communities (OHCs) have become a major source of support for people with health problems. This research tries to improve our understanding of social influence and to identify influential users in OHCs. The outcome can facilitate OHC management, improve community sustainability, and eventually benefit OHC users. Methods Through text mining and sentiment analysis of users' online interactions, the research revealed sentiment dynamics in threaded discussions. A novel metric—the number of influential responding replies—was proposed to directly measure a user's ability to affect the sentiment of others. Results 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.

Social bookmarking sites typically visualize user-generated tags as tag clouds. While tag clouds effectively show the relative frequency and thus popularity of tags, they fail to convey two aspects to the users: (1) the similarity between tags, and (2) the abstractness of tags. We suggest an alternative to tag clouds known as tag hierarchies. Tag hierarchies are based on a minimum evolution-based greedy algorithm for tag hierarchy construction, which iteratively includes optimal tags into the tree that introduce minimum changes to the existing taxonomy. Our algorithm also uses a global tag ranking method to order tags according to their levels of abstractness as well as popularity such that more abstract tags will appear at higher levels in the taxonomy. Based on the tag hierarchy, we derive a new tag recommendation algorithm, which is a structure-based approach that does not require heavily trained models and thus is highly efficient. User studies and quantitative analysis suggest that (1) the tag hierarchy can potentially reduce the user's tagging time in comparison to tag clouds and other tag tree structures, and (2) the tag recommendation algorithm significantly outperforms existing content-based methods in quality.

In many social networks, the connections between actors are formed because they participate in the same event, such as a set of scholars coauthoring a paper and a person making phone calls or having teleconferences with his friends. Therefore, we propose an event-driven framework for creating network growth models. We also notice that in evolving networks, both the behavior of the whole network and the behavior of nodes evolve over time. For example, we observe in collaborative networks that the growth rates of the communities and the average number of coauthors in papers change as the network sizes increase over time, and researchers' interactions with local groups and remote groups also evolve over time with their experience (degree). These observations motivate us to propose a behavior evolution-aware event-driven locality and attachedness based model to capture the growth dynamics in social networks. Based on some informative metrics of network structure and properties, such as degree distribution, degree-dependent clustering coefficients, and degree-dependent average degree of neighbors, the experiments suggest that our model can better characterize the growing process and simulate important network structures observed in real networks than other non-event driven and non-behavior aware models.

In evolving social networks, nodes join, make connections, or leave over time. In this paper, we introduce node event sequences that record activities of every node over time. Node event sequences are suitable for microscopic analysis of node behaviors in social networks. As preliminary results of taking advantage of node event sequences (as well as snapshots of networks), we study the health of the community of Nanotechnology based on the analysis of Seniority of nodes, identify the intrinsic dynamics of formation of new edges and its relation to Seniority of nodes, and the changes in the node behavior according to the nodes' Seniority.

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 research interests among researchers from many disciplines for its increasing application in multiple, disparate areas, including computer science, biology, social science and so on. This paper describes an LDA(latent Dirichlet Allocation)-based hierarchical Bayesian algorithm, namely SSN-LDA (simple social network LDA). In SSN-LDA, communities are modeled as latent variables in the graphical model and defined as distributions over the social actor space. The advantage of SSN-LDA is that it only requires topological information as input. This model is evaluated on two research collaborative networkst: CtteSeer and NanoSCI. The experimental results demonstrate that this approach is promising for discovering community structures in large-scale networks.

The development of Web Information Systems(WIS) is a hot research topic both in industry and academia. This paper presents a platform for ontology driven WIS development: the Web Information System auto-construction Environment(WISE). The novel features of WISE are: 1) Employing WIS ontology to specify user requirements; 2) Providing graphical tools to support ontology construction; 3) Drawing user interfaces and generating source code automatically from WIS ontology.

The load shedding problem is vital to a Data Stream Management System (DSMS). This paper presents the design, implementation, and evaluation of a load shedding method under the guide of the feedback control theory, in order to solve practical problems in medical environment. Thus, the using of operator selectivity, which has been proven not stable enough, is avoided. This paper focuses on the restriction of memory resource, this prevents the overflow of both CUP and memory resource. Our method can well support ad-hoc queries, while it is not so in a DSMS using current load shedding method because of the instability of operator selectivity. Our method also ensures a higher query precision when the system is over loaded and is easy to be implemented. The analytical and experimental results show that our method can be applied to medical data stream systems efficiently.

This paper proposes a general consistency management methodology within ontology-driven WIS development. The contributions of this paper are follows: 1) introduces the concept structures for representing model ontologies, mappings ontology and their instances. These ontologies and their instances greatly enhance the representation and reasoning capabilities of mapping and also simplify the manipulation 2) Defines and implements a series of general operators to manipulate mapping and model ontologies. 3) Formalizes the consistency management problem and gives an algorithm basing on general operators. 4) Implements a prototype and integrates it with WISE.

Complex structure and varying requirements increase the difficulties in developing domain specific Web Information Systems. People appeal to a smart tool to customize Web Information Systems automatically. To achieve that goal, this article makes following contributions: (1) Proposes the concept of Web Information System ontology to express the integrated domain semantics and maintain their relationships as a hierarchal structure; (2) Implements the Personalized tool for Ontology Development in domain specific Web Information System(PODWIS) with algorithms for (a) leaning the user's behaviors while building the ontology, (b) mining the repeated contents and composing reusable components, (c) guiding developer's work efficiently with high quality; (3)Designs two different self-learning strategies: online learning and offline learning to meet different situations; (4) Gives a semi-automatic tool to generate the personalized information system(includes user interface, operations and database access) based on ontology; (5)Shows the feasibility of this technique in practical application.

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.