Evolution Of Online Social Networks Research Articles

Edge intelligence‐enabled dynamic overlapping community discovery and evolution prediction in social media data streams

AbstractEdge intelligence (EI) is recognized by academia and industry as one of the key emerging technologies for future cyber‐physical‐social systems (CPSS), which provides the ability to analyze data at edge rather than sending it to the cloud for analysis, and will be a key enabler to realize a world of a trillion hyper‐connected smart sensing devices. As a part of future CPSS, online social networks are large‐scale complex networks that consist of a large number of network nodes and links. The dynamic discovery of communities, especially overlapping communities, is important to understand the evolution of online social networks. However, traditional community discovery algorithms cannot effectively discover overlapping communities in social networks. In order to address this challenge, an edge intelligence‐enabled dynamic overlapping community discovery and evolution prediction model (EIDEP) is proposed in this article. This model encompasses a label propagation algorithm based extension (LPAE) algorithm, which is able to efficiently discover the user community structures in online social networks. Based on the LPAE community discovery algorithm, a user interest behavior based evolution prediction (UIBEP) algorithm is incorporated in our EIDEP model in order to realize a fast yet accurate community evolution for online social networks, by considering the interest similarity of unlinked nodes in a given community. The performance of our proposed LPAE and UIBEP models is validated and evaluated against notable state‐of‐the‐art community discovery algorithms, through extensive experiments conducted based on a Twitter dataset.

HM-EIICT: Fairness-aware link prediction in complex networks using community information

The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions.

How migrants manifest their transnational identity through online social networks: comparative findings from a case of Koreans in Germany

Although migration has long been an imperative topic in social sciences, there are still needs of study on migrants’ unique and dynamic transnational identity, which heavily influences the social integration in the host society. In Online Social Network (OSN), where the contemporary migrants actively communicate and share their stories the most, different challenges against migrants’ belonging and identity and how they cope or reconcile may evidently exist. This paper aims to scrutinise how migrants are manifesting their belonging and identity via different technological types of online social networks, to understand the relations between online social networks and migrants’ multi-faceted transnational identity. The research introduces a comparative case study on an online social movement led by Koreans in Germany via their online communities, triggered by a German TV advertisement considered as stereotyping East Asians given by white supremacy’s point of view. Starting with virtual ethnography on three OSNs representing each of internet generations (Web 1.0 ~ Web 3.0), two-step Qualitative Data Analysis is carried out to examine how Korean migrants manifest their belonging and identity via their views on “who we are” and “who are others”. The analysis reveals how Korean migrants’ transnational identities differ by their expectation on the audience and the members in each online social network, which indicates that the distinctive features of the online platform may encourage or discourage them in shaping transnational identity as a group identity. The paper concludes with the two main emphases: first, current OSNs comprising different generational technologies play a significant role in understanding the migrants’ dynamic social values, and particularly, transnational identities. Second, the dynamics of migrants’ transnational identity engages diverse social and situational contexts. (keywords: transnational identity, migrants’ online social networks, stereotyping migrants, technological evolution of online social network).

Characterizing reticulation in online social networks during disasters

Online social network has become a new form of infrastructure for communities in spreading situational information in disasters. Developing effective interventions to improve the network performance of information diffusion is essential for people to rapidly retrieve information in coping with disasters and subsequent disruptions. Existing studies have investigated multiple aspects of online social networks in stationary situations and a separate manner. However, the networks are dynamic and different properties of the networks are co-related in the evolving disaster situations. In particular, disaster events motivate people to communicate online, create and reinforce their connections, and lead to a dynamic reticulation of the online social networks. To understand the relationship among these elements, we proposed an Online Network Reticulation (ONR) framework to examine four modalities (i.e., enactment, activation, reticulation, and network performance) in the evolution of online social networks to analyze the interplays among disruptive events in disasters, user activities, and information diffusion performance on social media. Accordingly, we examine the temporal changes in four elements for characterization of reticulation: activity timing, activity types (post, share, reply), reticulation mechanism (creation of new links versus reinforcement of existing links), and structure of communication instances (self-loop, converging, and reciprocal). Finally, the aggregated effects of network reticulation, using attributed network-embedding approach, are examined in the average latent distance among users as a measure of network performance for information propagation. The application of the proposed framework is demonstrated in a study of network reticulation on Twitter for a built environment disruption event during 2017 Hurricane Harvey in Houston. The results show that the main underlying mechanism of network reticulation in evolving situations was the creation of new links by regular users. The main structure for communication instances was converging, indicating communication instances driven by information-seeking behaviors in the wake of a disruptive event. With the evolution of the network, the proportion of converging structures to self-loop and reciprocal structures did not change significantly, indicating the existence of a scale-invariance property for network structures. The findings demonstrate the capability of the proposed online network reticulation framework for characterizing the complex relationships between events, activities, and network performance in online social networks during disasters.

Semantic approach using unified and summarised ontologies for analysing data from social media

Aggregating and analysing web social data is an important and interesting issue having an added value in various domains. Nevertheless, a major challenge to this issue is how to aggregate huge data scattered over a multitude of social media and be able to meet different analysis requirements and objectives such as recommendation, community detection, link prediction and sentiment analysis. In this context, we propose to use a summarised ontology of different inferred metrics that could be mutually reused to perform various analysis processes without redundant computing. According to the continuous evolution of online social networks (OSN), these metrics are dynamically inferred from a unified semantic model that extends standard ontologies used in Social web field. The proposed extension allows representing and aggregating data from a multitude of the most popular OSN.

Semantic approach using unified and summarised ontologies for analysing data from social media

Aggregating and analysing web social data is an important and interesting issue having an added value in various domains. Nevertheless, a major challenge to this issue is how to aggregate huge data scattered over a multitude of social media and be able to meet different analysis requirements and objectives such as recommendation, community detection, link prediction and sentiment analysis. In this context, we propose to use a summarised ontology of different inferred metrics that could be mutually reused to perform various analysis processes without redundant computing. According to the continuous evolution of online social networks (OSN), these metrics are dynamically inferred from a unified semantic model that extends standard ontologies used in Social web field. The proposed extension allows representing and aggregating data from a multitude of the most popular OSN.

Uncovering the nucleus of a massive reciprocal network

Google+ is a directed online social network where nodes have either reciprocal (bidirectional) edges or parasocial (one-way) edges. As reciprocal edges play an important role in the structural properties, formation and evolution of online social networks, we study the core structure of the subgraph formed by them, referred to as the reciprocal network of Google+ — in a sense, a reciprocal network can be viewed as the stable “skeleton” network of a directed online social network that holds it together. We develop an effective three-step procedure to hierarchically extract and unfold the core structure of a network by building up and generalizing ideas from the existing k-shell decomposition and clique percolation approaches. Our scheme produces higher-level representations of the core structure of the Google+ reciprocal network and it reveals that there are ten subgraphs (“communities”) comprising of dense clusters of cliques lying at the center of the core structure of the Google+ reciprocal network. Together they form the core to which “peripheral” sparse subgraphs are attached. Furthermore, our analysis shows that the core structure of the Google+ reciprocal network is very stable as the network evolves. Our results have implications in the design of algorithms for information flow, and in development of techniques for analyzing the vulnerability or robustness of online social networks.

Mapping social identity change in online networks of addiction recovery

Sustainable addiction recovery is determined in part by how social and community resources can be mobilised to support long-term identity change. Given the current growth in technology, we ask what the role of online social interactions is in supporting long-term identity change for people in recovery. The paper also explores the relationship between the evolution of online social networks and key events that members experience in the outside world, based on a project examining changes in online participation over eight months among members of a UK addiction recovery community built around a social enterprise for employment and housing. The social enterprise had an open Facebook page that was used by staff, clients and by a diverse range of individuals not directly involved in the organisation. Based on an analysis of naturally occurring online data on the Facebook page, social network analysis (SNA) and computerised linguistic analysis that quantified emotion and belonging language in posts and subsequent ‘likes’, we found that variations in the structure of the online social network and the content of communication are consistent with ‘core’ members’ experience of those events. Our findings indicate that strong recovery networks supported by positive social interactions can contribute to achieving long-term identity change that supports sustaining engagement in recovery communities.

Understanding the process of social network evolution: Online-offline integrated analysis of social tie formation.

It is important to consider the interweaving nature of online and offline social networks when we examine social network evolution. However, it is difficult to find any research that examines the process of social tie formation from an integrated perspective. In our study, we quantitatively measure offline interactions and examine the corresponding evolution of online social network in order to understand the significance of interrelationship between online and offline social factors in generating social ties. We analyze the radio signal strength indicator sensor data from a series of social events to understand offline interactions among the participants and measure the structural attributes of their existing online Facebook social networks. By monitoring the changes in their online social networks before and after offline interactions in a series of social events, we verify that the ability to develop an offline interaction into an online friendship is tied to the number of social connections that participants previously had, while the presence of shared mutual friends between a pair of participants disrupts potential new connections within the pre-designed offline social events. Thus, while our integrative approach enables us to confirm the theory of preferential attachment in the process of network formation, the common neighbor theory is not supported. Our dual-dimensional network analysis allows us to observe the actual process of social network evolution rather than to make predictions based on the assumption of self-organizing networks.

A generalized theory of preferential linking

There are diverse mechanisms driving the evolution of social networks. A key open question dealing with understanding their evolution is: How do various preferential linking mechanisms produce networks with different features? In this paper we first empirically study preferential linking phenomena in an evolving online social network, find and validate the linear preference. We propose an analyzable model which captures the real growth process of the network and reveals the underlying mechanism dominating its evolution. Furthermore based on preferential linking we propose a generalized model reproducing the evolution of online social networks, and present unified analytical results describing network characteristics for 27 preference scenarios. We study the mathematical structure of degree distributions and find that within the framework of preferential linking analytical degree distributions can only be the combinations of finite kinds of functions which are related to rational, logarithmic and inverse tangent functions, and extremely complex network structure will emerge even for very simple sublinear preferential linking. This work not only provides a verifiable origin for the emergence of various network characteristics in social networks, but bridges the micro individuals’ behaviors and the global organization of social networks.

From sparse to dense and from assortative to disassortative in online social networks.

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.

Modeling dynamics of attention in social media with user efficiency

Evolution of online social networks is driven by the need of their members to shareand consume content, resulting in a complex interplay between individual activity andattention received from others. In a context of increasing information overload andlimited resources, discovering which are the most successful behavioral patterns toattract attention is very important. To shed light on the matter, we look into thepatterns of activity and popularity of users in the Yahoo Meme microblogging service.We observe that a combination of different type of social and content-producingactivity is necessary to attract attention and the efficiency of users, namely theaverage attention received per piece of content published, for many users has adefined trend in its temporal footprint. The analysis of the user time series ofefficiency shows different classes of users whose different activity patterns giveinsights on the type of behavior that pays off best in terms of attention gathering.In particular, sharing content with high spreading potential and then supporting theattention raised by it with social activity emerges as a frequent pattern for usersgaining efficiency over time.

Using data from online social networks in conservation science: which species engage people the most on Twitter?

Knowledge about the level of public attention toward different species is crucial to successful conservation. The evolution of online social networks offers new possibilities for collecting data about public interest. Building on an analysis of text messages on Twitter, this study aimed to quantify the level of public attention toward different mammal and bird species listed (at the full species or subspecies/population level) under the United States’ Endangered Species Act (ESA). Once a month during 1 year, I searched recently posted messages (‘tweets’) for the common names of every listed species. The polar bear (Ursus maritimus) was by far the most tweeted species. The other most tweeted mammals were the American bison (Bison bison), brown bear (U. arctos), cougar (Puma concolor), killer whale (Orcinus orca), black bear (U. americanus) and West Indian manatee (Trichechus manatus), respectively. The three most frequently tweeted birds were the sandhill crane (Grus canadensis), whooping crane (G. americana) and spotted owl (Strix occidentalis). Some species, such as the manatee, right whale (Eubalaena glacialis), and gray wolf (Canis lupus) ranked higher when restricting the search to conservation contexts. The results suggest that Twitter users interact about a biased sample of ESA-listed species: mammals were better represented than birds among the most tweeted species and larger-sized species received more tweets. The findings can be used for prioritizing conservation education and marketing campaigns aiming to raise the profile of lesser-known listed species. Data from online social networks open the door for a range of novel applications in conservation science.

Evolution of a large online social network

Although recently there are extensive research on the collaborative networks and online communities, there is very limited knowledge about the actual evolution of the online social networks (OSN). In the Letter, we study the structural evolution of a large online virtual community. We find that the scale growth of the OSN shows non-trivial S shape which may provide a proper exemplification for Bass diffusion model. We reveal that the evolutions of many network properties, such as density, clustering, heterogeneity and modularity, show non-monotone feature, and shrink phenomenon occurs for the path length and diameter of the network. Furthermore, the OSN underwent a transition from degree assortativity characteristic of collaborative networks to degree disassortativity characteristic of many OSNs. Our study has revealed the evolutionary pattern of interpersonal interactions in a specific population and provided a valuable platform for theoretical modeling and further analysis.