Social Network Based Reputation Computation and Document Classification

We develop two social network based algorithms that automatically compute author reputation from a collection of textual documents.We first extract keyword reference behaviors of the authors to construct a social network, which represents relationships among the authors in terms of information reference behavior. With this network, we apply the two algorithms: the first computes each author’s reputation value considering only direct reference and the second utilizes indirect reference recursively. We compare the reputation values computed by the two algorithms and reputation ratings given by a human domain expert. We further evaluate the algorithms in email categorization tasks by comparing them with machine learning techniques. Finally, we analyse the social network through a community detection algorithm and other analysis techniques. We observed several interesting phenomena including the network being scale-free and having a negative assortativity

The Unification and Assessment of Multi-Objective Clustering Results of Categorical Datasets with H-Confidence Metric

Abstract: Multi objective clustering is one focused area of multi objective optimization. Multi objective optimization attracted many researchers in several areas over a decade. Utilizing multi objective clustering mainly considers multiple objectives simultaneously and results with several natural clustering solutions. Obtained result set suggests different point of views for solving the clustering problem. This paper assumes all potential solutions belong to different experts and in overall; ensemble of solutions finally has been utilized for finding the final natural clustering. We have tested on categorical datasets and compared them against single objective clustering result in terms of purity and distance measure of k-modes clustering. Our clustering results have been assessed to find the most natural clustering. Our results get hold of existing classes decided by human experts

Social Bridge: searching beyond Friend of a Friend networks

Title from PDF of title page, viewed on June 11, 2012VitaIncludes bibliographic references (p. 102-104)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2012Social networking has turned into an integral constituent in our lives. There appears to be an imperative demand for finding and linking with others to share one's day-to-day activities. However, currently available search engines for social networking have limited features, such as searches for people mainly by name or finding people within a single domain. With the increasing popularity and complexity of social networks, there is a high demand to enhance current social networks with more advanced features such as, finding people according to their common interests, interaction patterns, or linking someone across domains beyond Friend of a Friend (FOAF) networks. This thesis aims to develop a social search engine, called the Social Bridge that dynamically generates an integrated social profile that portrays a user's profile of interests and interactions with others and helps him/her in connecting to others who share these common interests and interactions. The Social Bridge expands the FOAF concept of current social networking by defining the social strength that represents the degree of affability among people. Social Bridge is based on the integrated profiles of social networks generated by the level of interactions between friends and their respective interests (e.g., friends, likes, hash tags, etc.) extracted from their Twitter and Facebook profiles. The Social Bridge engine has been implemented using advanced methods and techniques including Information Retrieval Techniques (TF/IDF) and Fuzzy Logic. The Social Bridge framework is compared with the existing traditional social networking models and the proposed algorithms have proven to be powerful and efficient in finding potential friends for large social networks. The Social Bridge framework has been further evaluated through a survey of social network users for their feedback on its genuineness, correctness, and scalability.Introduction -- Related work -- Social Bridge overview -- Social Bridge framework -- Evaluation -- Case study: Social Bridge for clinical trials -- Conclusion and future wor

Integrating Social Networks for Context Fusion in Mobile Service Platforms

It is important for mobile service providers to be aware of user contexts and to provide contextually relevant mobile services to users. Thereby, in this paper, we propose a novel mechanism for integrating online social networks, which are regarded as an important channel for exchanging and propagating contexts. To efficiently discover personal contexts of certain users, the contexts of their neighbors can be fused to provide mobile recommendation services to mobile subscribers. However, since the social network of each user is distributed across several systems, it has been difficult to integrate contexts from distributed social networks. Thereby, we mobilize all possible on- and off-line social networks to build an ego-centric social network. We implemented the proposed system by collecting the social network dataset from online sources (e.g., Facebook, Twitter, CyWorld, and co-authoring patterns in major Korean journals) and offline (e.g., co-participation patterns in a number of Korean domestic conferences). After the system was implemented, we provided mobile services to conference participants by sending text messages about time schedules of relevant presentations

Integrating Social Networks for Context Fusion in Mobile Service Platforms

It is important for mobile service providers to be aware of user contexts and to provide contextually relevant mobile services to users. Thereby, in this paper, we propose a novel mechanism for integrating online social networks, which are regarded as an important channel for exchanging and propagating contexts. To efficiently discover personal contexts of certain users, the contexts of their neighbors can be fused to provide mobile recommendation services to mobile subscribers. However, since the social network of each user is distributed across several systems, it has been difficult to integrate contexts from distributed social networks. Thereby, we mobilize all possible on- and off-line social networks to build an ego-centric social network. We implemented the proposed system by collecting the social network dataset from online sources (e.g., Facebook, Twitter, CyWorld, and co-authoring patterns in major Korean journals) and offline (e.g., co-participation patterns in a number of Korean domestic conferences). After the system was implemented, we provided mobile services to conference participants by sending text messages about time schedules of relevant presentations