Semi-Supervised Overlapping Community Finding based on Label Propagation with Pairwise Constraints

Algorithms for detecting communities in complex networks are generally unsupervised, relying solely on the structure of the network. However, these methods can often fail to uncover meaningful groupings that reflect the underlying communities in the data, particularly when those structures are highly overlapping. One way to improve the usefulness of these algorithms is by incorporating additional background information, which can be used as a source of constraints to direct the community detection process. In this work, we explore the potential of semi-supervised strategies to improve algorithms for finding overlapping communities in networks. Specifically, we propose a new method, based on label propagation, for finding communities using a limited number of pairwise constraints. Evaluations on synthetic and real-world datasets demonstrate the potential of this approach for uncovering meaningful community structures in cases where each node can potentially belong to more than one community.Comment: Fix table

Overlapping modularity at the critical point of k-clique percolation

One of the most remarkable social phenomena is the formation of communities in social networks corresponding to families, friendship circles, work teams, etc. Since people usually belong to several different communities at the same time, the induced overlaps result in an extremely complicated web of the communities themselves. Thus, uncovering the intricate community structure of social networks is a non-trivial task with great potential for practical applications, gaining a notable interest in the recent years. The Clique Percolation Method (CPM) is one of the earliest overlapping community finding methods, which was already used in the analysis of several different social networks. In this approach the communities correspond to k-clique percolation clusters, and the general heuristic for setting the parameters of the method is to tune the system just below the critical point of k-clique percolation. However, this rule is based on simple physical principles and its validity was never subject to quantitative analysis. Here we examine the quality of the partitioning in the vicinity of the critical point using recently introduced overlapping modularity measures. According to our results on real social- and other networks, the overlapping modularities show a maximum close to the critical point, justifying the original criteria for the optimal parameter settings.Comment: 20 pages, 6 figure

Parallel Community Detection Based on Distance Dynamics for Large-Scale Network

© 2013 IEEE. Data mining task is a challenge on finding a high-quality community structure from large-scale networks. The distance dynamics model was proved to be active on regular-size network community, but it is difficult to discover the community structure effectively from the large-scale network (0.1-1 billion edges), due to the limit of machine hardware and high time complexity. In this paper, we proposed a parallel community detection algorithm based on the distance dynamics model called P-Attractor, which is capable of handling the detection problem of large networks community. Our algorithm first developed a graph partitioning method to divide large network into lots of sub-networks, yet maintaining the complete neighbor structure of the original network. Then, the traditional distance dynamics model was improved by the dynamic interaction process to simulate the distance evolution of each sub-network. Finally, we discovered the real community structure by removing all external edges after evolution process. In our extensive experiments on multiple synthetic networks and real-world networks, the results showed the effectiveness and efficiency of P-Attractor, and the execution time on 4 threads and 32 threads are around 10 and 2 h, respectively. Our proposed algorithm is potential to discover community from a billion-scale network, such as Uk-2007

Collective intelligence: aggregation of information from neighbors in a guessing game

Complex systems show the capacity to aggregate information and to display coordinated activity. In the case of social systems the interaction of different individuals leads to the emergence of norms, trends in political positions, opinions, cultural traits, and even scientific progress. Examples of collective behavior can be observed in activities like the Wikipedia and Linux, where individuals aggregate their knowledge for the benefit of the community, and citizen science, where the potential of collectives to solve complex problems is exploited. Here, we conducted an online experiment to investigate the performance of a collective when solving a guessing problem in which each actor is endowed with partial information and placed as the nodes of an interaction network. We measure the performance of the collective in terms of the temporal evolution of the accuracy, finding no statistical difference in the performance for two classes of networks, regular lattices and random networks. We also determine that a Bayesian description captures the behavior pattern the individuals follow in aggregating information from neighbors to make decisions. In comparison with other simple decision models, the strategy followed by the players reveals a suboptimal performance of the collective. Our contribution provides the basis for the micro-macro connection between individual based descriptions and collective phenomena.Comment: 9 pages, 9 figure

Early Warning Analysis for Social Diffusion Events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks

Gay-Related Smartphone Applications: Potential and Risk- A Review of the Medical Literature in the Field

In the setting of difficulty in finding inter-relational partners for the individuals belonging to the LGBT community, a number of mobile phone applications provided with geo-tracking system have appeared in the last years, facilitating communication between gay individuals located in the nearby geographic areas, free applications which protect the identity of the users and indicate the relative distance between users and allow the sharing of information regarding physical characteristics (age, height, weight) as well as image-type files. At present, it is worth acknowledging that all those applications addressed to the LGBT persons in search for partners are sex-specific, being polarized (applications for MSM/lesbians). Among those the most successful and renowned are Grindr, Planet Romeo (homosexuals, MSM) and, respectively, Brenda (lesbians). In the medical literature there are recent studies that assess the STI risk-specific profile of these users, as well as the opportunities of behavioral study that these applications are presenting to the scientists, through the accessibility of interviewing gay persons and targeting them in HIV prevention programs. The present paper aims to look over the medical studies published to date which involved these types of internet type social networks, emphasizing on the potential represented by these applications and on the behavioral and risk profile of the users

Exploring the virtual space of academia

The aim of this chapter is to provide a view on how researchers present themselves in a social network specifically developed for supporting academic practices, how they share information and engage in dialogues with colleagues worldwide. We analysed data from 30,428 users who have registered on a publicly available website to study the effect of academic position, university ranking and country on people's behaviour. Results suggest that the virtual network closely mirrors physical reality, reproducing the same hierarchical structure imposed by position, ranking, and country on user behaviour. Despite the potential for bridging and bonding social capital the networks have not achieved substantial changes in structures and practices of the academic context. Furthermore, our analysis highlights the need of finding new strategies to motivate the users to contribute to the community and support equal participation, as so far the community is mainly exploited as a static website

Researching health in diverse neighbourhoods: critical reflection on the use of a community research model in Uppsala, Sweden.

OBJECTIVE: A community research model developed in the United Kingdom was adopted in a multi-country study of health in diverse neighbourhoods in European cities, including Sweden. This paper describes the challenges and opportunities of using this model in Sweden. RESULTS: In Sweden, five community researchers were recruited and trained to facilitate access to diverse groups in the two study neighbourhoods, including ethnic, religious, and linguistic minorities. Community researchers recruited participants from the neighbourhoods, and assisted during semi-structured interviews. Their local networks, and knowledge were invaluable for contextualising the study and finding participants. Various factors made it difficult to fully apply the model in Sweden. The study took place when an unprecedented number of asylum-seekers were arriving in Sweden, and potential collaborators' time was taken up in meeting their needs. Employment on short-term, temporary contracts is difficult since Swedish Universities are public authorities. Strong expectations of stable full-time employment, make flexible part-time work undesirable. The community research model was only partly successful in embedding the research project as a collaboration between community members and the University. While there was interest and some involvement from neighbourhood residents, the research remained University-led with a limited sense of community ownership

Enhanced Performance Cooperative Localization Wireless Sensor Networks Based on Received-Signal-Strength Method and ACLM

There has been a rise in research interest in wireless sensor networks (WSNs) due to the potential for his or her widespread use in many various areas like home automation, security, environmental monitoring, and lots more. Wireless sensor network (WSN) localization is a very important and fundamental problem that has received a great deal of attention from the WSN research community. Determining the relative coordinate of sensor nodes within the network adds way more aiming to sense data. The research community is extremely rich in proposals to deal with this challenge in WSN. This paper explores the varied techniques proposed to deal with the acquisition of location information in WSN. In the study of the research paper finding the performance in WSN and those techniques supported the energy consumption in mobile nodes in WSN, needed to implement the technique and localization accuracy (error rate) and discuss some open issues for future research. The thought behind Internet of things is that the interconnection of the Internet-enabled things or devices to every other and human to realize some common goals. WSN localization is a lively research area with tons of proposals in terms of algorithms and techniques. Centralized localization techniques estimate every sensor node's situation on a network from a central Base Station, finding absolute or relative coordinates (positioning) with or without a reference node, usually called the anchor (beacon) node. Our proposed method minimization error rate and finding the absolute position of nodes