Inferring Mobility Relationship via Graph Embedding

A framework for exploring organizational structure in dynamic social networks

Detection of community structures in social networks has attracted lots of attention in the domain of sociology and behavioral sciences. Social networks also exhibit dynamic nature as these networks change continuously with the passage of time. Social networks might also present a hierarchical structure led by individuals who play important roles in a society such as managers and decision makers. Detection and visualization of these networks that are changing over time is a challenging problem where communities change as a function of events taking place in the society and the role people play in it. In this paper, we address these issues by presenting a system to analyze dynamic social networks. The proposed system is based on dynamic graph discretization and graph clustering. The system allows detection of major structural changes taking place in social communities over time and reveals hierarchies by identifying influential people in social networks. We use two different data sets for the empirical evaluation and observe that our system helps to discover interesting facts about the social and hierarchical structures present in these social networks.

Literature have focused on studying the apparent and latent interactions within social graphs as an n‐ary operation, which yields binary outputs comprising positives (friends, likes, etc.) and negatives (foes, dislikes, etc.). Inasmuch as interactions constitute the bedrock of any given social network (SN) structure; there exist scenarios where an interaction, which was once considered a positive, transmutes into a negative as a result of one or more indicators which have affected the interaction quality. At present, this transmutation has to be manually executed by the affected actors in the SN. These manual transmutations can be quite inefficient, ineffective, and a mishap might have been incurred by the constituent actors and the SN structure prior to a resolution. Our problem statement aims at automatically flagging positive ties that should be considered for breakups or rifts (negative‐tie state), as they tend to pose potential threats to actors and the SN. Therefore, we have proposed ClasReg: a unique framework capable of breakup and link predictions.

The social network structure at the interface between a firm's business and IT units is of vital importance to business/IT alignment and hence an important IT governance object. Yet, there is a substantial gap in understanding the nature of these social structures and how they affect IT business value. Based on a literature review that comprises articles published in eight top IS journals since 2000, this paper identifies social network structure characteristics which represent antecedents of alignment. These are subsequently translated into precise concepts of social network analysis (SNA). The identification and formalization of such social network structures allow IT governance to implement social engineering mechanisms in order to influence the social network structure and thus business/IT alignment and IT value. Examples for such translated arguments are a high degree centrality of CIOs vis-a-vis other executives and strong ties in terms of cross-domain knowledge between the CIO and other executives.

The aim of this study was to profile the social networks of young adult stroke survivors with aphasia. A case series approach using four persons with aphasia (PWA) younger than 50 years was utilized. Semistructured interviews were completed with the PWA and their primary caregiver/family to explore the most salient relationships in the PWA's social network. PWAs in this study exhibited very small social networks with the most salient relationships consisting of immediate family members. Mothers were the primary caregiver for the two most impaired PWA. Prestroke friendships were dissolved or reduced at the onset of aphasia. The most impaired PWAs spent most of their days in isolation without a consistent communication partner. This study suggests young adult stroke survivors with aphasia have significant changes in their social network structure after stroke, but it is unclear if the observed change in social network structure is more related to age or level of communication impairment.

The forwarding activities of Pocket Switched Network (PSN) under social scene has attracted increasing attention, which are deeply affected by the social features of PSN nodes, especially the social network structure and characteristics of selfishness among nodes that jointly construct their behavior model under an opportunistic forwarding environment. Through both analysis and simulation, we investigate how social network structure and node selfishness influence data forwarding activities of PSN nodes based on our proposed contact-and-cooperative model. Simulation results obtained by PROPHET algorithm show that PSN forwarding performance, including delay time and packet delivery ratio, are improved by the introduction of social network structure. Further, the differentiation of node selfishness according to nodes' relationships on social structure level also displays intrinsic benefit in network performance in the presence of the node selfish behavior that is inevitable in real world. The results also show that hub nodes in the network, which undertake heavy relay mission, may play a more important role in forwarding activities under our model with social selfishness compared with unconditionally cooperative cases.

With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the information that explicitly reflects user preference. In contrast, this paper proposes to utilize data that explicitly reflect user preference and include these data in a deep learning-based process to better capture user preference. Based on the self-attention network, this paper utilizes the attributes of the month of the check-ins and the categories of check-ins during this time, which indicate the periodicity of the user’s work and life and can reflect the habits of users. Moreover, considering that distance has a significant impact on a user’s decision of whether to visit a POI, we used a filter to remove candidate POIs that were more than a certain distance away when recommending the next POIs. We use check-in data from New York City (NYC) and Tokyo (TKY) as datasets, and experiments show that these improvements improve the recommended performance of the next POI. Compared with the state-of-the-art methods, the proposed method improved the recall rate by 7.32% on average.

User dynamics in online social networks have a significant impact not only on the online community but also on real-world activities. Therefore, understanding online user dynamics has become a significant issue. The wave equation-based model for online social networks (called the oscillation model) is a theoretical model proposed to describe user dynamics in online social networks. This model can be used to understand the relationship between explosive user dynamics and the structure of social networks. However, since the oscillation model was introduced as a purely theoretical model of social networks, it is necessary to confirm whether the model describes real phenomena correctly or not. In this paper, we first show a prediction from the oscillation model; the low-frequency oscillation mode of user dynamics will be dominant when the structure of online social networks changes so that user dynamics is activated. To verify the predictions with actual data, we show spectral analyses of both the log data of posts on an electronic bulletin board site and the frequency data of word search from Google Trends. The results support the predictions from the theoretical model.

Point-of-interest (POI) recommendations in location-based social networks (LBSNs) allow online users to discover various POIs for social activities occurring in the near future close to their current locations. Research has verified that people’s preferences regarding POIs are significantly affected by various internal and external contextual factors, which are therefore worth extensive study for POI recommendation. However, although psychological effects have also been demonstrated to be significantly correlated with an individual’s preferences, such effects have been largely ignored in previous studies on POI recommendation. For this paper, inspired by the famous memory theory in psychology, we were interested in whether memory-based preferences could be derived from users’ check-in data. Furthermore, we investigated how to incorporate these memory-based preferences into an effective POI recommendation scheme. Consequently, we refer to Ebbinghaus’s theory on memory, which describes the attenuation of an individual’s memory in the form of a forgetting curve over time. We first created a memory-based POI preference attenuation model and then adopted it to evaluate individuals’ check-ins. Next, we employed the memory-based values of check-ins to calculate the POI preference similarity between users in an LBSN. Finally, based on this memory-based preference similarity, we developed a novel POI recommendation method. We experimentally evaluated the proposed method on a real LBSN data set crawled from Foursquare. The results demonstrate that our method, which incorporates the proposed memory-based preference similarity for POI recommendation, significantly outperforms other methods. In addition, we found the best value of the parameter H in the memory-based preference model that optimizes the recommendation performance. This value of H implies that an individual’s memory usually has an effect on their daily travel choices for approximately 300 days.

Modular and hierarchical structure of social contact networks

Mobile phone carriers face challenges from three synergistic dimensions: Wireless, social, and mobile. Despite significant advances that have been made about social networks and human mobility, respectively, our knowledge about the interplay between two layers remains largely limited, partly due to the difficulty in obtaining large-scale datasets that could offer at the same time social and mobile information across a substantial population over an extended period of time. In this paper, we take advantage of a massive, longitudinal mobile phone dataset that consists of human mobility and social network information simultaneously, allowing us to explore the impact of human mobility patterns on the underlying social network. We find that human mobility plays an important role in shaping both local and global structural properties of social network. In contrast to the lack of scale in social networks and human movements, we discovered a characteristic distance in physical space between 10 and 20 km that impacts both local clustering and modular structure in social network. We also find a surprising distinction in trajectory overlap that segments social ties into two categories. Our results are of fundamental relevance to quantitative studies of human behavior, and could serve as the basis of anchoring potential theoretical models of human behavior and building and developing new applications using social and mobile technologies.

A social network is a web that integrates multiple levels of interindividual social relationships and has direct associations with an individual’s health and well-being. Previous research has mainly focused on how brain and social network structures (structural properties) act on each other and on how the brain supports the spread of ideas and behaviors within social networks (functional properties). The structure of the social network is correlated with activity in the amygdala, which links decoding and interpreting social signals and social values. The structure also relies on the mentalizing network, which is central to an individual’s ability to infer the mental states of others. Network functional properties depend on multilayer brain-social networks, indicating that information transmission is supported by the default mode system, the valuation system, and the mentalizing system. From the perspective of neuroendocrinology, overwhelming evidence shows that variations in oxytocin, β-endorphin and dopamine receptor genes, including oxytocin receptor (OXTR), mu opioid receptor 1 (OPRM1) and dopamine receptor 2 (DRD2), predict an individual’s social network structure, whereas oxytocin also contributes to improved transmission of emotional and behavioral information from person to person. Overall, previous studies have comprehensively revealed the effects of the brain, endocrine system, and genes on social networks. Future studies are required to determine the effects of cognitive abilities, such as memory, on social networks, the characteristics and neural mechanism of social networks in mental illness and how social networks change over time through the use of longitudinal methods.

Online Social Networks (OSN) are getting popular day by day. Users share their information in OSN with others users. Access control is required to prevent unauthorized access to this information. Several studies have been conducted for access control in social networks. This chapter is a survey of available access control models/techniques based on social networks. Available access control models can be categorized as relationship-based, attributes-based, community structure-based and user activity centric model. A number of techniques have been proposed by several authors for access control in social networks. Most of the approaches use Social Network Analysis (SNA) techniques, others use user related information, for example, attributes or activities, the rest use a combination of approaches.

Research on growth of innovations introduced to the market has gradually shifted its focus from aggregate-level diffusion to exploring how growth is influenced by a given social network structure's characteristics. In this paper, we critically review this branch of literature. We argue that the growth of an innovation in a social network is shaped by the network's structure. Borrowing from the field of industrial organization in economics, which defines itself as the study of the effect of market structure on market performance, we describe this new wave of research on growth of innovations as the effect of social network structure on innovation performance. Hence, social network structural characteristics should be incorporated into research on new product growth as well as into managerial marketing decisions such as targeting and new product seeding.We review how social network structure influences innovations' market performance. Specifically, we discuss (1) a networks' global characteristics, namely average degree, degree distribution, clustering, and degree assortativity; (2) dyadic characteristics, or the relationships between pairs of network members, namely tie strength and embeddedness; (3) intrinsic individual characteristics, namely opinion leadership and susceptibility; and (4) location-based individual characteristics, namely the degree centrality, closeness centrality, and betweenness centrality of an individual network member.Overall, we find that growth is particularly effective in networks that demonstrate the “3 Cs”: cohesion (strong mutual influence among its members), connectedness (high number of ties), and conciseness (low redundancy). We identify gaps in current knowledge, discuss the implications on managerial decision making, and suggest topics for future research.

<p>The development of farmer groups in Indonesia is being stagnant because of the function of farmer group could not afford the needs of farmer group members. Participation of members is crucial to be assessed in order to promote the development of farmer group. To increase the participation of members, the social network structure between members and leaders should be taken into consideration. In this paper, the function of local institution leaders together with the function of farmer group leaders are measured in the social network structure. Through the graph of social network, it found that members will access information easily through the routine meeting in the local institution (neighborhood association) while the farmer group leaders are functioning as a legitimate of farmer group agenda. This paper suggests that the relationship between member and leader on the social network structure influences the member participation in the farmer group.</p>