Location Prediction: Communities Speak Louder than Friends

Humans are social animals, they interact with different communities of friends to conduct different activities. The literature shows that human mobility is constrained by their social relations. In this paper, we investigate the social impact of a person's communities on his mobility, instead of all friends from his online social networks. This study can be particularly useful, as certain social behaviors are influenced by specific communities but not all friends. To achieve our goal, we first develop a measure to characterize a person's social diversity, which we term `community entropy'. Through analysis of two real-life datasets, we demonstrate that a person's mobility is influenced only by a small fraction of his communities and the influence depends on the social contexts of the communities. We then exploit machine learning techniques to predict users' future movement based on their communities' information. Extensive experiments demonstrate the prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201

Selfishness, altruism and message spreading in mobile social networks

Many kinds of communication networks, in particular social and opportunistic networks, rely at least partly on on humans to help move data across the network. Human altruistic behavior is an important factor determining the feasibility of such a system. In this paper, we study the impact of different distributions of altruism on the throughput and delay of mobile social communication system. We evaluate the system performance using four experimental human mobility traces with uniform and community-biased traffic patterns. We found that mobile social networks are very robust to the distributions of altruism due to the nature of multiple paths. We further confirm the results by simulations on two popular social network models. To the best of our knowledge, this is the first complete study of the impact of altruism on mobile social networks, including the impact of topologies and traffic patterns.published_or_final_versio

HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process

The prevalence of location-based social networks (LBSNs) has eased the understanding of human mobility patterns. Knowledge of human dynamics can aid in various ways like urban planning, managing traffic congestion, personalized recommendation etc. These dynamics are influenced by factors like social impact, periodicity in mobility, spatial proximity, influence among users and semantic categories etc., which makes location modelling a critical task. However, categories which act as semantic characterization of the location, might be missing for some check-ins and can adversely affect modelling the mobility dynamics of users. At the same time, mobility patterns provide a cue on the missing semantic category. In this paper, we simultaneously address the problem of semantic annotation of locations and location adoption dynamics of users. We propose our model HAP-SAP, a latent spatio-temporal multivariate Hawkes process, which considers latent semantic category influences, and temporal and spatial mobility patterns of users. The model parameters and latent semantic categories are inferred using expectation-maximization algorithm, which uses Gibbs sampling to obtain posterior distribution over latent semantic categories. The inferred semantic categories can supplement our model on predicting the next check-in events by users. Our experiments on real datasets demonstrate the effectiveness of the proposed model for the semantic annotation and location adoption modelling tasks.Comment: 11 page

Migration Enclaves, Schooling Choices and Social Mobility

This paper investigates the presence of a network externality which might explain the persistence of low schooling achievements among internal migrants. A simple analytical framework is presented to show how an initial human capital disparity between migrants and non migrants can translate into persistent skill inequality if origin shapes the composition of social networks. We test empirically whether young migrants�schooling decisions are affected by the presence of covillagers at destination, using data on life-time histories of migration and education choices from a rural region of Thailand. Different modelling approaches are used to account for the self-selection of young migrants, for potential endogeneity of the network size, and for unobserved heterogeneity in individual preferences. The size of the migrant network is found to negatively affect the propensity of young migrants to pursue schooling while in the city. This fi�nding suggests that policies seeking to minimising strati�cation in enclaves might have a socially multiplied impact on schooling participation, and, ultimately, affect the socio-economic mobility of the rural born.human capital, schooling, networks, migration, inequality

Analysis of Community Behavior of Delay Tolerant Protocols

Abstract Delay tolerant networks constitute the category of Mobile Ad hoc Networks and are characterized by the ab-sence end-to-end path connectivity with limited data sources and power, where intermittent data communication is always a challenging task. To overcome this network partitioning, node mobility is exploited to increase message delivery in routing of these networks. Human mobility patterns have a great affect in increasing performance of routing protocol. In this paper, we have addressed, gathered and studied various routing protocols in DTNs that have used user mobility patterns for routing. These protocols use the constructive or destructive social characteristics for improving the performance in message forwarding. We have studied the impact of users social relationships on the protocols' performance

Traffic distribution and network capacity analysis in social opportunistic networks

Social opportunistic networks are intermittently connected mobile ad hoc networks (ICNs) that exploit human mobility to physically carry messages between disconnected parts of the network. Human mobility thus plays an essential role in the performance of forwarding protocols in the networks, and people's movements are in turn affected by their social interactions with each other. In this paper we present an analysis of the traffic distribution among the nodes of social opportunistic networks and its impact on network capacity. For our analysis, we use a human contact graph that represents a social network of individuals. We characterize the graph as a scale-free network and apply forwarding strategies based on the information required by a node to select relays for its messages, categorising this information either as isolated or complete network or local network knowledge. We use a social network property, centrality, for the forwarding strategies, additionally considering tie strength in the forwarding metric and investigate their impact on traffic distribution. We show that all the strategies result in unfair traffic distribution due to a strong non-random structure of the networks, where hub nodes process much more relay traffic than non-hub nodes. Finally, we present a mathematical model of network capacity as an upper-bound of network delivery performance where hub nodes' resources become the limiting factors, and show that including tie strength in the forwarding metric improves the network capacity

The Impact of Social Networks on Well-Being: Evidence from Latino

Presentation made at Latinos in the Heartland (9th : 2011 : Columbia, Mo.) and published in the annual conference proceedings.A series of studies has questioned the stylized fact that most Latino immigrants favor settling in major cities. The recent wave of immigration into the rural areas has been raising concerns about resource distribution and utilization. Special concerns have been expressed about having immigrants become a public burden thus, depleting local resources that could be employed elsewhere. Therefore, recently, monumental efforts have been put into Latinos' wellbeing research due to its potential to disperse widespread fears of opportunism by Latino immigrant and point out alternative avenues of integration into the community. Recent research has argued that immigrants are both important, as a workforce (Card, 2005; Jacobs, 1969), and detrimental, as free riders of social support services (Borjas, 1999), to the economic development of the receiving communities. However, the claim that Latino immigrants freeride on social welfare services to sustain their well-being seems a little bit confusing since current law does not provide for it; given the implementation of PRWORA. Thus, this study suggests that immigrants have been sustaining or improving well-being through social networks. This paper assesses the impact of social networks on well-being by combining sustainable livelihoods and household production theoretical frameworks. Specifically, emphasis is placed on assets and strategies Latino immigrants use to sustain and improve their well-being in non-urban areas of Missouri. Previous studies on well-being have focused on "economic" well-being thereby using income as a proxy. This study uses a much-expanded concept of well-being, which is subjectively assessed on a scale of 1 to 7, which includes various facets of human behavior. Thus, social network's impact is assessed empirically through ordered Probit regression using primary data from three different non-urban areas of Missouri. It is hypothesized that social networks have positive impact on Latino immigrant's well-being in these non-urban areas. This hypothesis has far-reaching implications in terms of local and regional policy focused on immigrants. For instance, local leaders can tap into these networks to pass important information related to education and economic mobility and opportunities for immigrants living in these areas

The effect of disruptive events on spatial and social interactions: An assessment of structural changes in pre-and post-COVID-19 pandemic networks

Disruptive events significantly alter spatial and social interactions among people and places. To examine the structural changes in spatial and social interaction networks in pre- and post-periods of the COVID-19 pandemic, we employ the Louvain method to algorithmically detect regions (communities) within the county-to-county networks of the SafeGraph mobility and Facebook social connectedness. We then utilize a range of partition similarity metrics, including adjusted Rand, z-Rand, Normalized Mutual Information (NMI), and Jaccard indices, to quantitatively measure the similarity of regions between the pre- and post-periods partitions of each network. Our findings reveal that in the post-pandemic period, spatial interactions led to the formation of localized geographic communities or regions characterized by higher modular activity within each region. In contrast, online social interactions shifted towards longer distance connections, resulting in the emergence of larger regions marked by strong friendship ties that often encompassed multiple states. By understanding these changes, we contribute to a better comprehension of the pandemic's impact on our interconnected physical-virtual world, providing valuable insights for future research and informing strategies to adapt to the evolving dynamics of human interactions.Comment: Proceedings of GIScience 2023 Workshop on Disruptive Movement Analysis, September 12, 2023, Leeds, U