Analysis of group evolution prediction in complex networks

In the world, in which acceptance and the identification with social communities are highly desired, the ability to predict evolution of groups over time appears to be a vital but very complex research problem. Therefore, we propose a new, adaptable, generic and mutli-stage method for Group Evolution Prediction (GEP) in complex networks, that facilitates reasoning about the future states of the recently discovered groups. The precise GEP modularity enabled us to carry out extensive and versatile empirical studies on many real-world complex / social networks to analyze the impact of numerous setups and parameters like time window type and size, group detection method, evolution chain length, prediction models, etc. Additionally, many new predictive features reflecting the group state at a given time have been identified and tested. Some other research problems like enriching learning evolution chains with external data have been analyzed as well

A Fast and Efficient Incremental Approach toward Dynamic Community Detection

Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature caters to the static use-case where the underlying network does not change. However, many emerging real-world use-cases give rise to a need to incorporate dynamic graphs as inputs. In this paper, we present a fast and efficient incremental approach toward dynamic community detection. The key contribution is a generic technique called $\Delta-screening$, which examines the most recent batch of changes made to an input graph and selects a subset of vertices to reevaluate for potential community (re)assignment. This technique can be incorporated into any of the community detection methods that use modularity as its objective function for clustering. For demonstration purposes, we incorporated the technique into two well-known community detection tools. Our experiments demonstrate that our new incremental approach is able to generate performance speedups without compromising on the output quality (despite its heuristic nature). For instance, on a real-world network with 63M temporal edges (over 12 time steps), our approach was able to complete in 1056 seconds, yielding a 3x speedup over a baseline implementation. In addition to demonstrating the performance benefits, we also show how to use our approach to delineate appropriate intervals of temporal resolutions at which to analyze an input network

ACCESS: An Inception Report

Imagine a world in which all groups of citizens coming together to realize some public benefit measure and communicate the character and consequences of their work. Imagine further that all those groups have adopted a common reporting system that enables their individual reports to be compared, thus creating powerful descriptions of the relative and collective performance of citizen association for public benefit. Imagine, too, that this common measuring and reporting carries across to all forms of public-private partnership and corporate social responsibility. This is the world envisioned by ACCESS.For the past 18 months a growing number of concerned actors have been meeting, studying, and testing opinion around one of the great structural weaknesses in the world's institutional infrastructure -- inefficient and weak social investment markets. This inception report sets out the results of this enquiry in the form of a proposal to establish a reporting standard for nonprofit organizations seeking to produce social, environmental and, increasingly, financial returns. The ACCESS Reporting standard is one important contribution to redressing a major global system weakness, but it is certainly not the only one. Nor is it one that can operate in isolation from other initiatives. Accordingly, the ACCESS proposed plan of work involves convening a global dialogue on NGO transparency, accountability and performance with the objective of promoting ACCESS and other practical solutions to the challenges of social investment and civil society accountability.This report sets out the background and rationale for these proposals. You will meet the ACCESS sponsors and pilot project partners. Parts of the report are descriptive and analytical but other parts are necessarily theoretical and technical in nature. We make no apology for this. Part of the reason that in 2003 the world does not yet have a reporting standard for social actors is that the theory and technique have not been mastered. For those with a strong orientation toward strategy and action, however, these aspects are presented as well

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

Community Structure Characterization

This entry discusses the problem of describing some communities identified in a complex network of interest, in a way allowing to interpret them. We suppose the community structure has already been detected through one of the many methods proposed in the literature. The question is then to know how to extract valuable information from this first result, in order to allow human interpretation. This requires subsequent processing, which we describe in the rest of this entry

Property and the Construction of the Information Economy: A Neo-Polanyian Ontology

This chapter considers the changing roles and forms of information property within the political economy of informational capitalism. I begin with an overview of the principal methods used in law and in media and communications studies, respectively, to study information property, considering both what each disciplinary cluster traditionally has emphasized and newer, hybrid directions. Next, I develop a three-part framework for analyzing information property as a set of emergent institutional formations that both work to produce and are themselves produced by other evolving political-economic arrangements. The framework considers patterns of change in existing legal institutions for intellectual property, the ongoing dematerialization and datafication of both traditional and new inputs to economic production, and the emerging logics of economic organization within which information resources (and property rights) are mobilized. Finally, I consider the implications of that framing for two very different contemporary information property projects, one relating to data flows within platform-based business models and the other to information commons

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors