A framework for joint community detection across multiple related networks

Community detection in networks is an active area of research with many practical applications. However, most of the early work in this area has focused on partitioning a single network or a bipartite graph into clusters/communities. With the rapid proliferation of online social media, it has become increasingly common for web users to have noticeable presence across multiple web sites. This raises the question whether it is possible to combine information from several networks to improve community detection. In this paper, we present a framework that identifies communities simultaneously across different networks and learns the correspondences between them. The framework is applicable to networks generated from multiple web sites as well as to those derived from heterogeneous nodes of the same web site. It also allows the incorporation of prior information about the potential relationships between the communities in different networks. Extensive experiments have been performed on both synthetic and real-life data sets to evaluate the effectiveness of our framework. Our results show superior performance of simultaneous community detection over three alternative methods, including normalized cut and matrix factorization on a single network or a bipartite graph.     

Adaptive decision-making frameworks for dynamic multi-agent organizational change

This article presents a capability called Adaptive Decision-Making Frameworks (ADMF) and shows that it can result in significantly improved system performance across run-time situation changes in a multi-agent system. Specifically, ADMF can result in improved and more robust performance compared to the use of a single static decision-making framework (DMF). The ADMF capability allows agents to dynamically adapt the DMF in which they participate to fit their run-time situation as it changes. A DMF identifies a set of agents and specifies the distribution of decision-making control and the authority to assign subtasks among these agents as they determine how a goal or set of goals should be achieved. The ADMF capability is a form of organizational adaptation and differs from previous approaches to organizational adaptation and dynamic coordination in that it is the first to allow dynamic and explicit manipulation of these DMF characteristics at run-time as variables controlling agent behavior. The approach proposed for selecting DMFs at run-time parameterizes all domain-specific knowledge as characteristics of the agents’ situation, so the approach is application-independent. The presented evaluation empirically shows that, for at least one multi-agent system, there is no one best DMF for multiple agents across run-time situational changes. Next, it motivates the further exploration of ADMF by showing that adapting DMFs to run-time variations in situation can result in improved overall system performance compared to static or random DMFs.     

Mixing local and global information for community detection in large networks

Clustering networks play a key role in many scientific fields, from Biology to Sociology and Computer Science. Some clustering approaches are called global because they exploit knowledge about the whole network topology. Vice versa, so-called local methods require only a partial knowledge of the network topology. Global approaches yield accurate results but do not scale well on large networks; local approaches, vice versa, are less accurate but computationally fast. We propose CONCLUDE (COmplex Network CLUster DEtection), a new clustering method that couples the accuracy of global approaches with the scalability of local methods. CONCLUDE generates random, non-backtracking walks of finite length to compute the importance of each edge in keeping the network connected, i.e., its edge centrality. Edge centralities allow for mapping vertices onto points of a Euclidean space and compute all-pairs distances between vertices; those distances are then used to partition the network into clusters.     

A defence scheme against Identity Theft Attack based on multiple social networks

Recently, on-line social networking sites become more and more popular. People like to share their personal information such as their name, birthday and photos on these public sites. However, personal information could be misused by attackers. One kind of attacks called Identity Theft Attack is addressed in on-line social networking sites. After collecting the personal information of a victim, the attacker can create a fake identity to impersonate this victim and cheat the victim’s friends in order to destroy the trust relationships on the on-line social networking sites. In this paper, we propose a scheme to protect users from Identity Theft Attacks. In our work, users’ personal information can be still kept public. It means that this scheme does not violate the nature of the social networks. Compared with previous works, the proposed scheme incurs less overhead for users. Experimental results also demonstrate the practicality of the proposed scheme.     

Self-regularized causal structure discovery for trajectory-based networks

Trajectory-based networks exhibit strong heterogeneous patterns amid human behaviors. We propose a notion of causal time-varying dynamic Bayesian network (cTVDBN) to efficiently discover such patterns. While asymmetric kernels are used to make the model better adherence to causal principles, the variations of network connectivities are addressed by an adaptive over-fitting control. Compact regularization paths are obtained by approximate homotopy to make the solution tractable. In our experiments, cTVDBN structure discovery has successfully revealed the evolution of time-varying relationships in a ring road system, and provided insights for plausible road structure improvements from a traffic flow dataset.     

A dynamic methodology and associated tools to assess organizational capabilities

The assessment of organizational capabilities becomes a great challenge in extended and flexible organizations. This assessment is generally independent from the evaluation of operational results and could become isolated from the rest of the global performance system and face validity issues.     

在线社会网络的k-shell结构研究

分析研究了Twitter与You Tube两个在线社会网络的结构。用k-shell(k-壳)分解法对网络分解,并对比分析了它们的入(出)度、入(出)k-shell、以及度与k-shell之间的关系,发现它们之间有较大的差异。You Tube的入(出)度、入(出)k-shell分布均服从幂律分布,而Twitter的分布服从漂移幂律分布、指数截断的幂律分布,但它们的度与k-shell关系基本相同,都未表现出较强的相关性。此外,根据度相关系数的定义还提出k-shell相关性的定义及其计算方法,并用来刻画网络k-shell之间的同(异)配性。     

A videosharing social networking intervention for young adult cancer survivors

Clinicians interested in taking a proactive approach to healthy cancer survivorship might consider the use of a social networking and videosharing platform tailored specifically for young adult cancer survivors. This study examines six key factors that may influence a childhood cancer survivor’s participation in a social networking and videosharing intervention program tailored to their needs: (1) the individual’s social capital, defined as resources accessed by individuals through a broad range of social connections, (2) social support, (3) family interaction, (4) self-efficacy, (5) depression, and (6) self-reported quality of life. Fourteen healthy childhood cancer survivors participated in a social networking and videosharing intervention program, LIFECommunity, over a period of 6 months. Young adult cancer survivors with weak “bonding” social capital with other cancer survivors, little social support from friends and family, and lower family interaction participated in the social networking intervention more than those with stronger social capital and larger bases of support. The findings suggest that cancer survivors used the social network as a way to fulfill needs that were not being met in their “offline” lives. The study provides a deeper understanding of the factors that contribute to the success of social networking interventions for young cancer survivors.     

Emerging organizational structure for knowledge-oriented teamwork using genetic algorithm

Organizations have historically sought efficiency improvements through different combinations of materials, components, production and processes to get better performance. However, in this age of the knowledge economy, the new organizational management has shifted its focus to the proper use of the knowledge of employees to create greater output and performance. There is a recent trend towards flat organizations and team-orientated structures, therefore this study will concentrate on the knowledge-oriented teamwork. To construct the fitting team structure, we solve the problem in two stages. In the first stage, we assign the proper tasks to the proper members to achieve a good match for effective usage of organizational knowledge. In the second stage, we solve the problem of insufficient knowledge within the organizational structure generated in the first stage by adjusting the positions of members to improve the mutual coordination and knowledge sharing and support.We applied a basic genetic algorithm (BGA) to solve the problems in both the stages. Five factors, such as member/task number, the number of knowledge types, the number of task types, the average complexity of each member’s knowledge types and the average complexity of task knowledge types, are considered to generate different types of problems. Computational results show that the BGA is able to find optimal knowledge matching for small-sized problems in the first stage, and that the BGA is able to improve the organizational structure generated in the first stage in order to reduce the communication cost of knowledge support among the members in the second stage.     

An ontology-based analysis and semantics for organizational structure modeling in the ARIS method

This paper focuses on the issue of ontological analysis for organizational structure modeling in the ARIS method with the following contributions: (i) an interpretation of the language in terms of real-world entities in the UFO foundational ontology; (ii) the identification of inappropriate elements of the language, using a systematic ontology-based analysis approach; and (iii) recommendations for improvements of the language to resolve the issues identified.     

D2SNet: Dynamics of diffusion and dynamic human behaviour in social networks

In this paper, we present an original and formal framework, the D2SNet model designed to combine both the social network evolution and the diffusion dynamics among individuals. We have conducted experiments on three social networks that show identical characteristics as real social networks. A formal definition of the model is provided and we describe its implementation in a simulation tool. We represent human behaviors and information dissemination strategies by standard and synthetic scheme. In a first step, we study the impact of network growing strategies only and we highlight important parameters such as the evolution speed and mainly the kind of strategies that favour or not the spread. Then we study a more complete evolution strategy that involves link creation and deletion. We provide a deep analysis on the impact of each parameter such as evolution speed, creation and deletion probabilities and dynamic human behaviors on the diffusion amplitude and coverage. Our study gives a novel and useful insight in the diffusion process in a dynamic context.     

A new clustering method and its application in social networks

In a graph theory model, clustering is the process of division of vertices into groups, with a higher density of edges within groups than between them. In this paper, we introduce a new clustering method for detecting such groups and use it to analyse some classic social networks. The new method has two distinguished features: non-binary hierarchical tree and the feature of overlapping clustering. A non-binary hierarchical tree is much smaller than the binary-trees constructed by most traditional methods and, therefore, it clearly highlights meaningful clusters which significantly reduces further manual efforts for cluster selections. The present method is tested by several bench mark data sets for which the community structure was known beforehand and the results indicate that it is a sensitive and accurate method for extracting community structure from social networks.     

Structural reproduction of social networks in computer-mediated communication forums

This study explores the relationship between the structure of an existing social network and the structure of an emergent discussion-board network in an undergraduate university class. Thirty-one students were issued with laptop computers that remained in their possession for the duration of the semester. While using these machines, participants' email log files were collected directly from the University's email servers. The analysis compared structural attributes of actors evident in their social network with the emergent structural properties measured from interactions in a shared discussion board environment. A significant relationship was found between the existing social network structure and the emergent communication patterns, suggesting that existing relationships have a strong influence on subsequent computer-mediated communication. Additional matrices were used to control for gender, major and perceived computer efficacy, none of which had a significant effect.     

Searching for people to follow in social networks

With the development of social networks, more and more users have a great need to search for people to follow (SPTF) to receive their tweets. According to our experiments, approximately 50% of social networks’ lost users leave due to a lack of people to follow. In this paper, we define the problem of SPTF and propose an approach to give users tags and then deliver a ranked list of valuable accounts for them to follow. In the proposed approach, we first seek accounts related to keywords via expanding and predicting tags for users. Second, we propose two algorithms to rank relevant accounts: the first mines the forwarded relationship, and the second incorporates the following relationship into PageRank. Accordingly, we have built a search system¹ that to date, has received more than 1.7 million queries from 0.2 million users. To evaluate the proposed approach, we created a crowd-sourcing organization and crawled 0.25 billion profiles, 15 billion messages and 20 billion links representing following relationships on Sina Microblog. The empirical study validates the effectiveness of our algorithms for expanding and predicting tags compared to the baseline. From query logs, we discover that hot queries include keywords related to academics, occupations and companies. Experiments on those queries show that PageRank-like algorithms perform best for occupation-related queries, forward-relationship-like algorithms work best for academic-related queries and domain-related headcount algorithms work best for company-related queries.     

A dynamic fault-tolerant message routing algorithm for double-loop networks

Message routing is a fundamental function of a network, and fault-tolerance is an important tool to ensure the quality of service of a network. Assume that the network contains at most one faulty element and the algorithm does not know the faulty element in advance. We present an optimal fault-tolerant message routing algorithm for double-loop networks. We show that sending at most two messages with different routing strategies can ensure that one of the messages will be sent through a shortest path that avoids the faulty element. At each vertex, for any destination, the algorithm needs only constant time and space to determine the next vertex to which the message is to be sent.     

A social gamification framework for a K-6 learning platform

As video games, particularly, social games are growing in popularity and number of users, there has been an increasing interest in its potential as innovative teaching tools. Gamification is a new concept intending to use elements from video games in non-game applications. Education is an area with high potential for application of this concept since it seeks to promote people’s motivation and engagement. The research in progress will try to find how to apply social gamification in education, testing and validating the results of that application. To fulfil these objectives, this paper presents the guidelines and main features of a social gamification framework to be applied in an existent K-6 social learning environment.     

A framework for dynamic multiple-criteria decision making

The classic multiple-criteria decision making (MCDM) model assumes that, when taking a decision, the decision maker has defined a fixed set of criteria and is presented with a clear picture of all available alternatives. The task then reduces to computing the score of each alternative, thus producing a ranking, and choosing the one that maximizes this value.However, most real-world decisions take place in a dynamic environment, where the final decision is only taken at the end of some exploratory process. Exploration of the problem is often beneficial, in that it may unveil previously unconsidered alternatives or criteria, as well as render some of them unnecessary.In this paper we introduce a flexible framework for dynamic MCDM, based on the classic model, that can be applied to any dynamic decision process and which is illustrated by means of a small helicopter landing example. In addition, we outline a number of possible applications in very diverse fields, to highlight its versatility.     

Exploring album structure for face recognition in online social networks

In this paper, we propose an album-oriented face-recognition model that exploits the album structure for face recognition in online social networks. Albums, usually associated with pictures of a small group of people at a certain event or occasion, provide vital information that can be used to effectively reduce the possible list of candidate labels. We show how this intuition can be formalized into a model that expresses a prior on how albums tend to have many pictures of a small number of people. We also show how it can be extended to include other information available in a social network. Using two real-world datasets independently drawn from Facebook, we show that this model is broadly applicable and can significantly improve recognition rates.     

A randomized algorithm for the joining protocol in dynamic distributed networks

We describe a randomized algorithm for assigning neighbours to vertices joining a dynamic distributed network. The aim of the algorithm is to maintain connectivity, low diameter and constant vertex degree. On joining each vertex donates a constant number of tokens to the network. These tokens contain the address of the donor vertex. The tokens make independent random walks in the network. A token can be used by any vertex it is visiting to establish a connection to the donor vertex. This allows joining vertices to be allocated a random set of neighbours although the overall vertex membership of the network is unknown. The network we obtain in this way is robust under adversarial deletion of vertices and edges and actively reconnects itself.