Mapping communities in large virtual social networks: Using Twitter data to find the Indie Mac community

Finding a number of nodes that are able to maximize the spread of influence through the social network and are called influence maximization has numerous applications in marketing. One such application is to find influential members for promoting a product across a large network. Even though numerous algorithms have been proposed, challenges such as scalability, time constraints, and low accuracy have motivated the researchers for better solutions. Some of the newly proposed algorithms are scalable, but fail to provide adequate accuracy. On the other hand, some greedy algorithms provide a good level of accuracy but are very time consuming for large networks. In this paper, an algorithm is proposed called FAIMCS that can quickly find influential nodes across large networks with high accuracy. FAIMCS, reduces computational overhead considerably by eliminating major portions of the social network graph which have little influence. FAIMCS uses community detection algorithm to determine each community's quota of influential nodes based on the structure of that community. Finally, it obtains influential nodes from the candidate nodes. Experiment results show FAIMCS is faster than current algorithms and provides a high level of accuracy for large social networks.

A Characterization of Social Networks for Effective Communication and Collaboration in Computing EducationEffective communication is critical to the success of engineers in the workplace. Whileformalized communication is often our focus (e.g., creation of requirements specificationsand design documents, or delivery of formal presentations), informal communications (e.g.,impromptu meetings, water cooler discussions, etc.) are equally important. Other forms ofcommunication also exist, including the communication that is built into the common tasksof writing emails or status reports and the engineering tasks themselves, such as reading codeor writing comments. One emerging form of communication that has gained widespreadrecreational use are social networks such as Facebook and LinkedIn. With its popularitycontinually increasing, social networking has gone beyond sharing mundane details of one’spersonal life to enhancing collaboration and communication in corporate and educationaldomains. Indeed, the use of social networks has seen an increase in acceptance in the workforce.For example LinkedIn is widely used to maintain a list of contacts. Increasingly, socialnetworks are being used for day-to-day communication within the workplace in order help shareexpertise across an organization.Use of social networks by students is, for the most part, focused on the recreational, asexemplified by Facebook. However, for students, effective use of social networks forcollaborative purposes in the context of computing tasks and common genres of softwareengineering is still a work in progress. In this paper, we describe our experiences, observations,and recommendations regarding the use of social networks in the context of a computingcurriculum. Our approach is grounded in two desired objectives: to provide students withincreased access to technical knowledge and to facilitate communication between differentstakeholders on projects. Our experience on the use of social networks has encompassed avariety of platforms including Facebook, Twitter, and Google Plus. We have used the platformson a diverse set of courses in computer science, including courses on data structures, softwarearchitectures, web services, and the senior design/senior capstone where we have providedstudents with learning experiences that are not only relevant in the classroom but also closelymodel workplace activities. Based on our experience, we have categorized the strengths andweaknesses of using different social networks by looking at generic activities that happen withinthe workplace and then identifying how a particular social network supports a given activity.In addition to categorizing strengths and weaknesses, we have also made several observationsregarding the effectiveness of social networking in an educational setting. Specifically, we notethe following: a) that there are different motivating factors for students in using popular socialnetworking tools and b) that the degree of usage impacts academic achievement in differentcontexts. Although our observations are not scientific by any means, they still reflect interestingtrends which may have implications for future use of social networks in education and in turn inthe workplace.

Modern transportation has given rise to and facilitated the movement and mobility of populations. While much critical attention has been drawn to the permanent migration of the mobile population, very few scholars in the field of sociolinguistics have stressed the temporary movement of the group of travelers who are subsumed under the appellation, “the sojourner”. In addition, previous research predominantly focused on non-English speakers sojourning to English-dominant countries (Haneda and Monobe, 2009; Lee, 2008; Own, 1999), thus largely neglecting the multilingual contexts in Asia. To fill this scholarly gap, small-scale research was conducted by employing two frameworks, Social Network Theory (Milroy, 1980) and Community of Practice (Wenger, 1998; Eckert & McConnell-Ginet, 1992), to examine linguistic practices and identity positioning of Southeast Asian (SEA) sojourners when they interact with Hongkongers and other sojourners. In particular, it tackles a more complex language contact situation in which two major lingua francas, namely English and Mandarin, are available. This study was conducted in a higher education dance school in Hong Kong and three SEA sojourn students were recruited. Multi-faceted identities and multiple communities of practices are found: (1) at Communal Level: Cantonese is the shared linguistic repertoire of this dance community and three SEA sojourn students form the weakest ties with local students; (2) at Group Level: both Mandarin and English are adopted and stronger ties are established with other overseas sojourn students; and (3) at Individual Level: Singapore English is the dominant code choice used among these three SEA sojourners and they are bound together by the strongest ties. Instances of trilingual code-mixing and code-switching are also found in the interaction among the sojourners and Hong Kong locals. The two theories, Social Network Theory and Community of Practice, are complementary in accounting for the social organization of multilingual communities. Ultimately, this study demonstrates the complexity of multilingual communities with the aforesaid language contact in Hong Kong as a case in point.

A key challenge in the social network community is the problem of network generation -- that is, how can we create synthetic networks that match characteristics traditionally found in most real world networks? Important characteristics that are present in social networks include a power law degree distribution, small diameter, and large amounts of clustering. However, most current network generators, such as the Chung Lu and Kronecker models, largely ignore the clustering present in a graph and focus on preserving other network statistics, such as the power law distribution. Models such as the exponential random graph model have a transitivity parameter that can capture clustering, but they are computationally difficult to learn, making scaling to large real world networks intractable. In this work, we propose an extension to the Chung Lu random graph model, the Transitive Chung Lu (TCL) model, which incorporates the notion transitive edges. Specifically, it combines the standard Chung Lu model with edges that are formed through transitive closure (e.g., by connecting a 'friend of a friend'). We prove TCL's expected degree distribution is equal to the degree distribution of the original input graph, while still providing the ability to capture the clustering in the network. The single parameter required by our model can be learned in seconds on graphs with millions of edges, networks can be generated in time that is linear in the number of edges. We demonstrate the performance of TCL on four real-world social networks, including an email dataset with hundreds of thousands of nodes and millions of edges, showing TCL generates graphs that match the degree distribution, clustering coefficients and hop plots of the original networks.

Laboratory studies of social behavior have typically focused on dyadic interactions occurring within a limited spatiotemporal context. However, this strategy prevents analyses of the dynamics of group social behavior and constrains identification of the biological pathways mediating individual differences in behavior. In the current study, we aimed to identify the spatiotemporal dynamics and hierarchical organization of a large social network of male mice. We also sought to determine if standard assays of social and exploratory behavior are predictive of social behavior in this social network and whether individual network position was associated with the mRNA expression of two plasticity-related genes, DNA methyltransferase 1 and 3a. Mice were observed to form a hierarchically organized social network and self-organized into two separate social network communities. Members of both communities exhibited distinct patterns of socio-spatial organization within the vivaria that was not limited to only agonistic interactions. We further established that exploratory and social behaviors in standard behavioral assays conducted prior to placing the mice into the large group was predictive of initial network position and behavior but were not associated with final social network position. Finally, we determined that social network position is associated with variation in mRNA levels of two neural plasticity genes, DNMT1 and DNMT3a, in the hippocampus but not the mPOA. This work demonstrates the importance of understanding the role of social context and complex social dynamics in determining the relationship between individual differences in social behavior and brain gene expression.

This dissertation consists of two original studies in social networks and one original study in political economy. In the first two chapters, I study (i) how social networks form, and (ii) how economic agents optimize their behaviors for a given network structure. In the last chapter, I examine how election rules affect individual voting decisions and ultimate election outcomes. In Chapter 1, Social Network Formation and Strategic Interaction in Large Networks, I present a dynamic network formation model that aims to explain why some empirical degree distributions exhibit the increasing hazard rate property (IHRP). In my model, a sequentially arriving node forms a link with one existing node through a bilateral agreement. newborn node prefers a highly linked node; however, the more links an existing node has, the more the marginal return from an additional link diminishes. I prove that the IHRP emerges if and only if the latter effect prevails over the former. I present two implications of the IHRP for strategic interactions in networks. First, when there is uncertainty about neighboring agents' connectivity, the IHRP guarantees that a unique Bayesian equilibrium exists in a network game with strategic complementarities. Second, the IHRP characterizes a monotone revenue-maximizing mechanism with allocative externalities. In Chapter 2, Monopoly Pricing and Diffusion of a (Social) Network Good, I present a model of dynamic pricing and diffusion of a network good sold by a monopolist. In the model, the network good is a subscription social network good. This means that in each period, each consumer has to pay a subscription price to use the good, and the utility derived from subscribing to the good increases as more of her neighboring consumers subscribe. Consumers myopically optimize their subscription decisions, and the monopolist chooses a sequence of subscription prices that maximizes his discounted sum of per-period profits. Three main results emerge. First, I characterize a unique steady state of the monopoly market. Second, I find that optimal sequences of subscription prices oscillate around the subscription price at the steady state as time passes. Third, I analyze how changes in the monopolist's discount factor and the density of the social network affect the subscription price, subscription rate, and deadweight loss at the steady state. In Chapter 3, A Model of Pre-Electoral Coalition Formation, I study how two different election rules, simple plurality (e.g., as in South Korea) and two-round runoff (e.g., as in France), affect political candidates’ incentives to form pre-electoral coalitions (PECs). In my model, three candidates compete for a single office, and two candidates can form a PEC. Since the candidates are both policy- and office-motivated, one candidate can incentivize the other candidate to withdraw his candidacy by choosing a joint policy platform. I find that PECs are more likely to form in plurality elections than in two-round runoff elections. I further examine how other electoral environments, such as ideological distance and pre-election polls, influence incentives to form PECs.

The conventional notion of community that favors a high ratio of internal edges to outbound edges becomes invalid when each vertex participates in multiple communities. Such a behavior is commonplace in social networks. The significant overlaps among communities make most existing community detection algorithms ineffective. The lack of effective and efficient tools resulted in very few empirical studies on large-scale detection and analyses of overlapping community structure in real social networks. We developed recently a scalable and accurate method called the Partial Community Merger Algorithm (PCMA) with linear complexity and demonstrated its effectiveness by analyzing two online social networks, Sina Weibo and Friendster, with 79.4 and 65.6 million vertices, respectively. Here, we report in-depth analyses of the 2.9 million communities detected by PCMA to uncover their complex overlapping structure. Each community usually overlaps with a significant number of other communities and has far more outbound edges than internal edges. Yet, the communities remain well separated from each other. Most vertices in a community are multi-membership vertices, and they can be at the core or the peripheral. Almost half of the entire network can be accounted for by an extremely dense network of communities, with the communities being the vertices and the overlaps being the edges. The empirical findings ask for rethinking the notion of community, especially the boundary of a community. Realizing that it is how the edges are organized that matters, the f-core is suggested as a suitable concept for overlapping community in social networks. The results shed new light on the understanding of overlapping community.

The article is devoted to the topical issue of identifying information threats in virtual communities of social networks and mechanisms for counteracting such threats. The process of organization of monitoring and content analysis of virtual communities in social networks is studied. The technology of detecting information threats of virtual communities in social networks and methods of their assessment have been developed. An approach to finding pages of discussions on social networks based on the Louvain method is proposed. The technology of determining the degree of information threat of the virtual community in social networks and ways to counteract the information threats of the virtual community have been developed.

Understanding the importance of links in transmitting information in a network can provide ways to hinder or postpone ongoing dynamical phenomena like the spreading of epidemic or the diffusion of information. In this work, we propose a new measure based on stochastic diffusion processes, the transmission centrality, that captures the importance of links by estimating the average number of nodes to whom they transfer information during a global spreading diffusion process. We propose a simple algorithmic solution to compute transmission centrality and to approximate it in very large networks at low computational cost. Finally we apply transmission centrality in the identification of weak ties in three large empirical social networks, showing that this metric outperforms other centrality measures in identifying links that drive spreading processes in a social network.

Community detection is a substantial technique to find out the relationship between nodes in complex networks. By understanding the behavior of elements in a community, one can predict the overall feature of the large scale social network. Detecting different communities in large scale network is a challenging task due to huge data size associated with such network. The main purpose of this paper is finding distinct communities. For this reason, in this paper after using limited Random Walk to detect nodes feature set, nodes that share higher common feature set form a community. Experimental results in real and artificial networks show, with great accuracy, that the proposed method succeeds to recover communities in the network.

Background: Previous studies have found older adults’ cognitive functions are strongly associated with their social networks, including memory. Yet, few studies have explored the influences of specific social network members, such as siblings and children. Further, little studies examined the impact of the size of older adults’ social networks. Hence, this study aimed to investigate how older adults’ relationships with their spouses, siblings, and children, as well as the size of their social networks, affect American older adults’ memory functions. Methods: Using the 2018 data from NHATS, 5547 samples were included. We adopted a multiple logistic regression model to test the impact of social support network sizes, and how associations of social support networks varied between spouses, siblings, and children. All models were calibrated for age, gender, education, income, and race/ethnicity. Results: Analysis showed that higher socioeconomic status (more education and without Medicaid), being female, and younger age were associated with increased odds of having good self-rated memory functions. Older adults with larger social support networks (>=3 individuals) were more likely to have better self-rated memory function (adjusted odds ratio, 1.182, p<0.05), while holding other variables. Having a spouse also increased odds of higher self-rating memory function, in contrast to having children. Conclusion: This study highlighted the importance of having a larger social network size for older adult’s memory function and indicated the necessity of developing intervention programs to expand older adults' social network size, especially for those with lower socioeconomic status.

In this paper we study and evaluate rumor-like methods for combating the spread of rumors on a social network. We model rumor spread as a diffusion process on a network and suggest the use of an process similar to the rumor process. We study two natural models by which these anti-rumors may arise. The main metrics we study are the belief time, i.e., the duration for which a person believes the rumor to be true and point of decline, i.e., point after which anti-rumor process dominates the rumor process. We evaluate our methods by simulating rumor spread and anti-rumor spread on a data set derived from the social networking site Twitter and on a synthetic network generated according to the Watts and Strogatz model. We find that the lifetime of a rumor increases if the delay in detecting it increases, and the relationship is at least linear. Further our findings show that coupling the detection and anti-rumor strategy by embedding agents in the network, we call them beacons, is an effective means of fighting the spread of rumor, even if these beacons do not share information.

In the era of Big Data the prevalence of networks of all kinds has grown dramatically, and analysing (mining) such networks to support decision-making processes has become an extremely important subject for research, typically with a view to some social and/or economic gain. This thesis describes research work within the theme of Movement Pattern Mining (MPM) as applied to large network data. MPM is a type of frequent pattern mining that provides observation into how information is exchanged between objects in large networks. In the context of the work described in this thesis, the focus is on how the concept of Movement Patterns (MPs) can be extracted from large networks efficiently and effectively, and how such movement patterns can best be utilised so as to predict future movement. The work describes how, by utilising big data facilities like Share/Distribute Memory Systems and Hadoop/MapReduce, novel data mining based techniques can be used, not only to extract MPs from large networks, but also how they can be utilised for prediction purposes. To this end, the works in this thesis are divided into two parts. The first part is concerned with an investigation of an efficient mechanism for MPM. The second part is concerned with the utilisation of the extracted MPs in the context of prediction. For evaluation purposes, two large network datasets were used: The Great Britain Cattle Tracking System database and the Jiayuan Social Network. The evaluation indicates that an efficient and effective mechanism for identifying and extracting MPs form large networks, and subsequently using then MPs for prediction purposes, has been established.

Enumeration of all maximal bicliques in bipartite graphs is a well-studied fundamental problem. However, a wide range of applications need less overlapping bicliques with specific size constraints instead of all the maximal bicliques. In this paper, we study a new biclique problem, called the top-k t-biclique coverage problem. A t-biclique is a biclique with a size constraint <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> for one vertex set and the problem aims to find <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> t-bicliques maximising the coverage on the other vertex set. The top-k t-biclique coverage problem has novel applications such as finding top-k courses while maximising student engagement. We prove that this problem is NP-hard. A straightforward way to address the problem first needs to enumerate and store all t-bicliques and then greedily select <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> promising t-bicliques, leading an approximate guarantee on the coverage. However, it takes exponential space, which is impractical. We then apply a fast approximation scheme to solve this problem, which shaves the exponential space consumption by progressively updating top-k results during the t-biclique enumeration. Observing that the fast approximation algorithm takes too much time on updating the results due to the coverage is computed from scratch for each update, an online index is devised to address the drawback. Due the hardness of the problem, even the fast approximation algorithm cannot scale to large dataset. To devise a scalable solution, we then propose a heuristic algorithm running in polynomial time. Thanks for four carefully designed heuristic rules, the heuristic algorithm can find large coverage top-k t-bicliques extremely fast for large datasets. Apart from that, the heuristic result with large coverage can effectively prune unpromising enumerations in the fast greedy algorithm, which improves the efficiency of the fast approximation algorithm without compromising the approximation ratio. Extensive experiments are conducted on real datasets to justify the effectiveness and efficiency of the proposed algorithms.

Communities in social networks form with different purposes and play a significant role in interpersonal interactions. Analysis of virtual communities indicates a more precise understanding of the behaviours and desires of individuals in social networks. In this paper, new measures have been proposed for analyzing implicit and explicit communities in Online Social Networks (OSNs). The measures of “potential value of the community members” and “value of the community messages”, which are used for calculating the measure of “community value” are among the most important measures introduced in this paper. Another measure introduced is “user influence rate” in a community, which represents the contribution of a person in creating value in a community. To provide a sound dataset, we collected the information from several real implicit communities in Twitter based on different hashtags. Finally, the suggested measures have been analyzed and compared statistically and behaviourally across different communities. The results of this research well indicate the importance and practicality of the measures introduced in Community analysis of Twitter.