On-line social networks are complex ensembles of inter-linked communities that interact on different topics. Some communities are characterized by what are usually referred to as deviant behaviors, conducts that are commonly considered inappropriate with respect to the society's norms or moral standards. Eating disorders, drug use, and adult content consumption are just a few examples. We refer to such communities as deviant networks. It is commonly believed that such deviant networks are niche, isolated social groups, whose activity is well separated from the mainstream social-media life. According to this assumption, research studies have mostly considered them in isolation. In this work we focused on adult content consumption networks, which are present in many on-line social media and in the Web in general. We found that few small and densely connected communities are responsible for most of the content production. Differently from previous work, we studied how such communities interact with the whole social network. We found that the produced content flows to the rest of the network mostly directly or through bridge-communities, reaching at least 450 times more users. We also show that a large fraction of the users can be inadvertently exposed to such content through indirect content resharing. We also discuss a demographic analysis of the producers and consumers networks. Finally, we show that it is easily possible to identify a few core users to radically uproot the diffusion process. We aim at setting the basis to study deviant communities in context.
Computer science research has dealt extensively with the problem of classification of groups along structural, temporal, behavioral, and topical dimensions @cite_27 @cite_22 @cite_7 . The relationship between group connectivity and shape of information cascades has also been explored, revealing an intertwinement between community boundaries and cascade reach that is particularly tight in communities built upon a common theme shared by all of their members @cite_31 @cite_3 @cite_9 @cite_17 . The degree of inter-community interaction has been analyzed mostly in the context of heavily polarized networks, the most classical example being online discussions between two opposing political views @cite_28 @cite_0 @cite_10 . These studies explored methods to quantify segregation @cite_29 , but mainly focus on networks formed by two main divergent clusters.
@cite_22 @cite_7 @cite_28 @cite_9 @cite_29 @cite_3 @cite_0 @cite_27 @cite_31 @cite_10 @cite_17
2164998314 2077353658 1976160665 2963368130
To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network—including physics, chemistry, molecular biology, and medicine—information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences. Recent years have witnessed increased interests in topic detection and tracking (TDT). However, existing work mainly focuses on overall trend analysis, and is not developed for understanding the evolving process of topics. To this end, this paper aims to reveal the underlying process and reasons for topic formation and development (TFD). Along this line, based on community partitioning in social networks, a core-group model is proposed to explain the dynamics and to segment topic development. This model is inspired by the cell division mechanism in biology. Furthermore, according to the division phase and interphase in the life cycle of a core group, a topic is separated into four states including birth state, extending state, saturation state and shrinkage state. In this paper, we mainly focus our studies on scientific topic formation and development using the citation network structure among scientific papers. Experimental results on two real-world data sets show that the division of a core group brings on the generation of a new scientific topic. The results also reveal that the progress of an entire scientific topic is closely correlated to the growth of a core group during its interphase. Finally, we demonstrate the effectiveness of the proposed method in several real-life scenarios. Graph clustering has been widely applied in exploring regularities emerging in relational data. Recently, the rapid development of network theory correlates graph clustering with the detection of community structure, a common and important topological characteristic of networks. Most existing methods investigate the community structure at a single topological scale. However, as shown by empirical studies, the community structure of real world networks often exhibits multiple topological descriptions, corresponding to the clustering at different resolutions. Furthermore, the detection of multiscale community structure is heavily affected by the heterogeneous distribution of node degree. It is very challenging to detect multiscale community structure in heterogeneous networks. In this paper, we propose a novel, unified framework for detecting community structure from the perspective of dimensionality reduction. Based on the framework, we first prove that the well-known Laplacian matrix for network partition and the widely-used modularity matrix for community detection are two kinds of covariance matrices used in dimensionality reduction. We then propose a novel method to detect communities at multiple topological scales within our framework. We further show that existing algorithms fail to deal with heterogeneous node degrees. We develop a novel method to handle heterogeneity of networks by introducing a rescaling transformation into the covariance matrices in our framework. Extensive tests on real world and artificial networks demonstrate that the proposed correlation matrices significantly outperform Laplacian and modularity matrices in terms of their ability to identify multiscale community structure in heterogeneous networks. Research into socio-technical systems like Wikipedia has overlooked important structural patterns in the coordination of distributed work. This paper argues for a conceptual reorientation towards sequences as a fundamental unit of analysis for understanding work routines in online knowledge collaboration. We outline a research agenda for researchers in computer-supported cooperative work (CSCW) to understand the relationships, patterns, antecedents, and consequences of sequential behavior using methods already developed in fields like bio-informatics. Using a data set of 37,515 revisions from 16,616 unique editors to 96 Wikipedia articles as a case study, we analyze the prevalence and significance of different sequences of editing patterns. We illustrate the mixed method potential of sequence approaches by interpreting the frequent patterns as general classes of behavioral motifs. We conclude by discussing the methodological opportunities for using sequence analysis for expanding existing approaches to analyzing and theorizing about co-production routines in online knowledge collaboration.