Large-scale Online Social Networks Research Articles

Statistical study of characteristics of online reading behavior networks in university digital library

Composed of a large number of interacting nodes, an online reading behavior network (ORBN) in a university digital library constitutes a large-scale online social network, which can be modeled as a complex system. Online social networks have been investigated intensively by many researchers over the past several years. However, there is little research on the online reading behaviors in university digital libraries. In this paper, we investigate the statistical characteristics of ORBNs in university digital libraries. This study reveals that the degree distribution of an online reading behavior network obeys the exponential distribution. In addition, the small-world phenomenon is observed in ORBNs. This study also compares the statistical characteristics of different types of ORBNs. The results show that the statistical characteristics of online reading behavior sub-networks remain consistent over multiple school years. However, over every four school years, differences are identified between the global ORBN and its sub-networks.

A data-locality-aware task scheduler for distributed social graph queries

For large-scale online social networks such as Facebook and Twitter, network analysis often uses graph queries to extract network information. Because of the work and memory required, usually such queries are performed in a distributed manner. However, most existing distributed graph computation systems optimize for offline graph analysis rather than online graph queries. The problem with this approach is that graph query tasks then must transfer a large volume of data and interactively answer queries within a short time frame. To resolve this, we propose a novel data-locality-aware task scheduling algorithm that optimizes interactive distributed graph queries. The scheduling algorithm jointly considers data placement and graph topology to reduce data transfer costs. After implementing the scheduling algorithm in a real-world distributed graph computation system, we evaluate the task scheduler’s effectiveness through simulations and real-life social graph queries. Results show that our scheduler reduces the querying time by one order of magnitude.

Distributed and scalable Sybil identification based on nearest neighbour approximation using big data analysis techniques

The problem of Sybil detection has been examined in multiple social media sources like Twitter, LinkedIn and Facebook. The detection of Sybils (fake accounts or social bots) across online social networks emerged as a major challenge due to the current improvement of different social networks, which are promptly generating a very huge data sets termed as big data. The open-source framework, spark-based distributed, fast and scalable nearest neighbor search (S-DFS-NNS) is proposed for profile-based fake account detection across large-scale online social networks. The proposed work performs an efficient parallel processing of the NN search problem. The performance of the k-nearest neighbor (k-NN) search significantly degrades for huge data sets, because the job is computationally hard. The framework is fast and adaptable to expansive, large-scale situations. By using in-memory computation, the suspected users are identified based on the novel private feature. The Spark-DFS-NN search technique provides a substantial performance development over the nearest neighbor computation in large-scale networks. The proposed framework is evaluated using detection accuracy which is able to expose and block a large fraction of suspicious accounts during account creation. The proposed S-DFS-NN framework maintains an approximately consistent and similar performance of 89–95% on the increase of attacks with a latency of 58 ms.

QMSampler: Joint Sampling of Multiple Networks with Quality Guarantee

Because Online Social Networks (OSNs) have become increasingly important in the last decade, they have motivated a great deal of research on Social Network Analysis (SNA). Currently, SNA algorithms are evaluated on real datasets obtained from large-scale OSNs, which are usually sampled by Breadth-First-Search (BFS), Random Walk (RW), or some variations of the latter. However, none of the released datasets provides any statistical guarantees on the difference between the sampled datasets and the ground truth. Moreover, all existing sampling algorithms only focus on sampling a single OSN, but each OSN is actually a sampling of a complete social network. Hence, even if the whole dataset from a single OSN is sampled, the results may still be skewed and may not fully reflect the properties of the complete social network. To address the above issues, we have made the first attempt to explore the joint sampling of multiple OSNs and propose an approach called Quality-guaranteed Multi-network Sampler (QMSampler) that can jointly sample multiple OSNs. QMSampler provides a statistical guarantee on the difference between the sampled real dataset and the ground truth (the perfect integration of all OSNs). Our experimental results demonstrate that the proposed approach generates a much smaller bias than any existing method. QMSampler has also been released as a free download.

CBL: Exploiting Community Based Locality for Efficient Content Search Service in Online Social Networks

Retrieving relevant data for users in online social network (OSN) systems is a challenging problem. Cassandra, a storage system used by popular OSN systems, such as Facebook and Twitter, relies on a DHT-based scheme to randomly partition the personal data of users among servers across multiple data centers. Although DHT is highly scalable for hosting a large number of users (personal data), it leads to costly inter-server communications across data centers due to the complex interconnection and interaction among OSN users. In this paper, we explore how to retrieve the OSN content in a cost-effective way by retaining the simple and robust nature of OSNs. Our approach exploits a simple, yet powerful principle called Community-Based Locality (CBL), which posits that if a user has a one-hop neighbor within a particular community, it is very likely that the user has other one-hop neighbors inside the same community. We demonstrate the existence of community-based locality in diverse traces of popular OSN systems such as Facebook, Orkut, Flickr, Youtube, and Livejournal. Based on the observation, we design a CBL-based algorithm to build the content index in OSN systems. By partitioning and indexing the relevant data of users within a community on the same server in the data center, the CBL-based index avoids a significant amount of inter-server communications during searching, making retrieving relevant data for a user in large-scale OSNs efficient. In addition, by using CBL-based scheme we can provide much faster search response and balanced loads. We conduct comprehensive trace-driven simulations to evaluate the performance of the proposed scheme. Results show that ourscheme significantly reduces the network traffic by 73 percent while reduces the query latency by 35 percent compared with existing schemes.

DRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networks

With the soaring development of large scale online social networks, online information sharing is becoming ubiquitous everyday. Various information is propagating through online social networks including both the positive and negative. In this paper, we focus on the negative information problems such as the online rumors. Rumor blocking is a serious problem in large-scale social networks. Malicious rumors could cause chaos in society and hence need to be blocked as soon as possible after being detected. In this paper, we propose a model of dynamic rumor influence minimization with user experience (DRIMUX). Our goal is to minimize the influence of the rumor (i.e., the number of users that have accepted and sent the rumor) by blocking a certain subset of nodes. A dynamic Ising propagation model considering both the global popularity and individual attraction of the rumor is presented based on a realistic scenario. In addition, different from existing problems of influence minimization, we take into account the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the blocking time of each user exceeds that threshold, the utility of the network will decrease. Under this constraint, we then formulate the problem as a network inference problem with survival theory, and propose solutions based on maximum likelihood principle. Experiments are implemented based on large-scale real world networks and validate the effectiveness of our method.

Modeling geo-homopholy in online social networks for population distribution projection

PurposeProjecting the population distribution in geographical regions is important for many applications such as launching marketing campaigns or enhancing the public safety in certain densely populated areas. Conventional studies require the collection of people’s trajectory data through offline means, which is limited in terms of cost and data availability. The wide use of online social network (OSN) apps over smartphones has provided the opportunities of devising a lightweight approach of conducting the study using the online data of smartphone apps. This paper aims to reveal the relationship between the online social networks and the offline communities, as well as to project the population distribution by modeling geo-homophily in the online social networks.Design/methodology/approachIn this paper, the authors propose the concept of geo-homophily in OSNs to determine how much the data of an OSN can help project the population distribution in a given division of geographical regions. Specifically, the authors establish a three-layered theoretic framework that first maps the online message diffusion among friends in the OSN to the offline population distribution over a given division of regions via a Dirichlet process and then projects the floating population across the regions.FindingsBy experiments over large-scale OSN data sets, the authors show that the proposed prediction models have a high prediction accuracy in characterizing the process of how the population distribution forms and how the floating population changes over time.Originality/valueThis paper tries to project population distribution by modeling geo-homophily in OSNs.

The Impact of Geographic Distance on Online Social Interactions

Online social networking services entice millions of users to spend hours every day interacting with each other. The focus of this work is to explain the effect that geographic distance has on online social interactions and, simultaneously, to understand the interplay between the social characteristics of friendship ties and their spatial properties. We analyze data from a large-scale online social network, Tuenti, with about 10 million active users: our sample includes user profiles, user home locations and online social interactions among Tuenti members. Our findings support the idea that spatial distance constraints whom users interact with, but not the intensity of their social interactions. Furthermore, friendship ties belonging to denser connected groups tend to arise at shorter spatial distances than social ties established between members belonging to different groups. Finally, we show that our findings mostly do not depend on the age of the users, although younger users seem to be slightly more constrained to shorter geographic distances. Augmenting social structure with geographic information adds a new dimension to social network analysis and a large number of theoretical investigations and practical applications can be pursued for online social systems, with many promising outcomes. As the amount of available location-based data is increasing, our findings and results open the door to future possibilities: researchers would benefit from these insights when studying online social services, while developers should be aware of these additional possibilities when building systems and applications related to online social platforms.

Identification of influential nodes based on temporal-aware modeling of multi-hop neighbor interactions for influence spread maximization

This paper presents the identification of highly influential nodes based on temporal-aware modeling of multi-hop neighbor interactions to maximize the spread of information in online social networks (OSNs). The objective is to choose a set of influential nodes that have higher temporal multi-hop interactions and more topological connections in large-scale OSNs to maximize information dissemination and minimize spreading time. An influence diffusion process that is solely based on topology is not able to capture the influence spreading efficiently. A temporal multi-hops social interaction based centrality is proposed to choose nodes of higher spreading ability considering the nodes’ neighbors and neighbors-of-neighbors temporal modeled interactions and topological connections. The temporal-aware interactions are modeled to find users who are more active recently. First, we model the influence between users considering the temporal interactions of the user and its neighbors. A subset of nodes with a higher influence value and more topological connections with direct neighbors is selected. Secondly, we select the Top-K higher influential spreader nodes from the subset of nodes considering the node neighbors and neighbors-of-neighbors temporal modeled social interactions and topological connections. Finally, the proposed algorithm is evaluated using the epidemic spreading models. The experimental results show that the algorithm is able to extract highly influential nodes that maximize the spread of information and minimize contagion time.

Fake profile detection techniques in large-scale online social networks: A comprehensive review

In the present era, online social networks are the most popular and rapid information propagation applications on the Internet. People of all ages spend most of their time on social networking sites. Huge volumes of data are being created and shared through social networks around the world. These interests have given rise to illegitimate users who engage in fraudulent activities against social network users. On social networks, fake profile creation is considered to cause more harm than any other form of cyber crime. This crime has to be detected even before the user is notified about the fake profile creation. Many algorithms and methods, most of which use the huge volume of unstructured data generated from social networks, have been proposed for the detection of fake profiles. This study presents a survey of the existing and latest technical work on fake profile detection.

The happiness paradox: your friends are happier than you

Most individuals in social networks experience a so-called Friendship Paradox: they are less popular than their friends on average. This effect may explain recent findings that widespread social network media use leads to reduced happiness. However the relation between popularity and happiness is poorly understood. A Friendship paradox does not necessarily imply a Happiness paradox where most individuals are less happy than their friends. Here we report the first direct observation of a significant Happiness Paradox in a large-scale online social network of 39,110 Twitter users. Our results reveal that popular individuals are indeed happier and that a majority of individuals experience a significant Happiness paradox. The magnitude of the latter effect is shaped by complex interactions between individual popularity, happiness, and the fact that users strongly cluster by similar level of happiness. Our results indicate that the topology of online social networks, combined with how happiness is distributed in some populations, may be associated with significant psycho-social effects.

A fast multi-level algorithm for community detection in directed online social networks

The discovery of underlying community structures plays a significant role in online social network (OSN) analysis. Many previous methods suffer from inaccuracy or incompleteness in community descriptions because of the multiple factors affecting OSNs and the high computational complexity caused by the large scale of these networks. We present a new community detection approach that focuses on two aspects. First, it relies on a combination of user interests and cohesiveness in describing community structures. Second, it introduces a multi-level community discovery algorithm for large-scale OSN datasets. The algorithm consists of three steps: (1) network coarsening based on the combination of two categories of properties, (2) stochastic inference to find an initial community assignment over the coarsest network and (3) projection and refinement of this assignment to obtain the final community detection result by solving a semi-supervised learning problem. The combination of user interests and cohesiveness leads to a complete and well-interpreted description of the communities embedded in OSNs, and the multi-level algorithm speeds up the computation process and improves the likelihood of finding the global optimal solution by reducing the parameter space. Experiments conducted on both synthetic and real datasets demonstrate the effectiveness and efficiency of our method.

Mechanism Design in Social Networks

This paper studies an auction design problem for a seller to sell a commodity in a social network, where each individual (the seller or a buyer) can only communicate with her neighbors. The challenge to the seller is to design a mechanism to incentivize the buyers, who are aware of the auction, to further propagate the information to their neighbors so that more buyers will participate in the auction and hence, the seller will be able to make a higher revenue. We propose a novel auction mechanism, called information diffusion mechanism (IDM), which incentivizes the buyers to not only truthfully report their valuations on the commodity to the seller, but also further propagate the auction information to all their neighbors. In comparison, the direct extension of the well-known Vickrey-Clarke-Groves (VCG) mechanism in social networks can also incentivize the information diffusion, but it will decrease the seller's revenue or even lead to a deficit sometimes. The formalization of the problem has not yet been addressed in the literature of mechanism design and our solution is very significant in the presence of large-scale online social networks.

Hashkat: large-scale simulations of online social networks

Hashkat (http://hashkat.org) is a free, open source, agent based simulation software package designed to simulate large-scale online social networks (e.g. Twitter, Facebook, LinkedIn, etc). It allows for dynamic agent generation, edge creation, and information propagation. The purpose of hashkat is to study the growth of online social networks and how information flows within them. Like real life online social networks, hashkat incorporates user relationships, information diffusion, and trending topics. Hashkat was implemented in C++, and was designed with extensibility in mind. The software includes Shell and Python scripts for easy installation and usability. In this report, we describe all of the algorithms and features integrated into hashkat before moving on to example use cases. In general, hashkat can be used to understand the underlying topology of social networks, validate sampling methods of such networks, develop business strategy for advertising on online social networks, and test new features of an online social network before going into production.

Homophilic network decomposition: a community-centric analysis of online social services

In this paper we formulate the homophilic network decomposition problem: Is it possible to identify a network partition whose structure is able to characterize the degree of homophily of its nodes? The aim of our work is to understand the relations between the homophily of individuals and the topological features expressed by specific network substructures. We apply several community detection algorithms on three large-scale online social networks—Skype, LastFM and Google+—and advocate the need of identifying the right algorithm for each specific network in order to extract a homophilic network decomposition. Our results show clear relations between the topological features of communities and the degree of homophily of their nodes in three online social scenarios: product engagement in the Skype network, number of listened songs on LastFM and homogeneous level of education among users of Google+.

Flexible sampling large-scale social networks by self-adjustable random walk

Abstract Online social networks (OSNs) have become an increasingly attractive gold mine for academic and commercial researchers. However, research on OSNs faces a number of difficult challenges. One bottleneck lies in the massive quantity and often unavailability of OSN population data. Sampling perhaps becomes the only feasible solution to the problems. How to draw samples that can represent the underlying OSNs has remained a formidable task because of a number of conceptual and methodological reasons. Especially, most of the empirically-driven studies on network sampling are confined to simulated data or sub-graph data, which are fundamentally different from real and complete-graph OSNs. In the current study, we propose a flexible sampling method, called Self-Adjustable Random Walk (SARW), and test it against with the population data of a real large-scale OSN. We evaluate the strengths of the sampling method in comparison with four prevailing methods, including uniform, breadth-first search (BFS), random walk (RW), and revised RW (i.e., MHRW) sampling. We try to mix both induced-edge and external-edge information of sampled nodes together in the same sampling process. Our results show that the SARW sampling method has been able to generate unbiased samples of OSNs with maximal precision and minimal cost. The study is helpful for the practice of OSN research by providing a highly needed sampling tools, for the methodological development of large-scale network sampling by comparative evaluations of existing sampling methods, and for the theoretical understanding of human networks by highlighting discrepancies and contradictions between existing knowledge/assumptions of large-scale real OSN data.

Íntegro: Leveraging victim prediction for robust fake account detection in large scale OSNs

Detecting fake accounts in online social networks (OSNs) protects both OSN operators and their users from various malicious activities. Most detection mechanisms attempt to classify user accounts as real (i.e., benign, honest) or fake (i.e., malicious, Sybil) by analyzing either user-level activities or graph-level structures. These mechanisms, however, are not robust against adversarial attacks in which fake accounts cloak their operation with patterns resembling real user behavior.In this article, we show that victims – real accounts whose users have accepted friend requests sent by fakes – form a distinct classification category that is useful for designing robust detection mechanisms. In particular, we present Íntegro – a robust and scalable defense system that leverages victim classification to rank most real accounts higher than fakes, so that OSN operators can take actions against low-ranking fake accounts. Íntegro starts by identifying potential victims from user-level activities using supervised machine learning. After that, it annotates the graph by assigning lower weights to edges incident to potential victims. Finally, Íntegro ranks user accounts based on the landing probability of a short random walk that starts from a known real account. As this walk is unlikely to traverse low-weight edges in a few steps and land on fakes, Íntegro achieves the desired ranking.We implemented Íntegro using widely-used, open-source distributed computing platforms, where it scaled nearly linearly. We evaluated Íntegro against SybilRank, which is the state-of-the-art in fake account detection, using real-world datasets and a large-scale deployment at Tuenti – the largest OSN in Spain with more than 15 million active users. We show that Íntegro significantly outperforms SybilRank in user ranking quality, with the only requirement that the employed victim classifier is better than random. Moreover, the deployment of Íntegro at Tuenti resulted in up to an order of magnitude higher precision in fake account detection, as compared to SybilRank.

Towards a standard sampling methodology on online social networks: collecting global trends on Twitter.

One of the most significant current challenges in large-scale online social networks, is to establish a concise and coherent method aimed to collect and summarize data. Sampling the content of an Online Social Network (OSN) plays an important role as a knowledge discovery tool.It is becoming increasingly difficult to ignore the fact that current sampling methods must cope with a lack of a full sampling frame i.e., there is an imposed condition determined by a limited data access. In addition, another key aspect to take into account is the huge amount of data generated by users of social networking services such as Twitter, which is perhaps the most influential microblogging service producing approximately 500 million tweets per day. In this context, due to the size of Twitter, which is problematic to be measured, the analysis of the entire network is infeasible and sampling is unavoidable.In addition, we strongly believe that there is a clear need to develop a new methodology to collect information on social networks (social mining). In this regard, we think that this paper introduces a set of random strategies that could be considered as a reliable alternative to gather global trends on Twitter. It is important to note that this research pretends to show some initial ideas in how convenient are random walks to extract information or global trends.The main purpose of this study, is to propose a suitable methodology to carry out an efficient collecting process via three random strategies: Brownian, Illusion and Reservoir. These random strategies will be applied through a Metropolis-Hastings Random Walk (MHRW). We show that interesting insights can be obtained by sampling emerging global trends on Twitter. The study also offers some important insights providing descriptive statistics and graphical description from the preliminary experiments.

Minimizing Inter-Server Communications by Exploiting Self-Similarity in Online Social Networks

Efficiently operating on relevant data for users in large-scale online social network (OSN) systems is a challenging problem. Storage systems used by popular OSNs often rely on key-value stores, where randomly partitioning the data of users among servers across the data centers is the defacto standard. Although by using DHTs, the random partition scheme is highly scalable for hosting a large number of users, it leads to costly inter-server communications across data centers due to the complexity of interconnection and interaction between OSN users. In this paper, we explore how to reduce the inter-server communications by retaining the simple and robust nature of OSNs. We propose a data placement solution atop OSN systems to divide users among servers according to the interaction-locality-based structure. Our approach exploits a simple, yet powerful principle of OSN interactions, self-similarity, which reveals that the inter-server communication cost is minimized under such intrinsic structure. Our algorithm avoids a significant amount of inter-server traffic as well as achieves load balance among servers across the data centers. We demonstrate the existence of self-similarity in large-scale Facebook traces including 10 million Facebook users and 24 million interaction events. We conduct comprehensive trace-driven simulations to evaluate this design. Results show that our scheme significantly reduces the traffic and latency of OSN systems comparing to existing schemes.

The Persistence of Division

As noted by theorists such as Blau, Durkheim, Mayhew, and others, interaction opportunity is a fundamental determinant of social structure. One of the most empirically well established factors influencing interaction opportunity is that of physical distance. The strength of this effect in modern societies, however, has been called into question because of technological advances (the so-called death-of-distance hypothesis). Here, the authors examine the effect of distance in an extreme case, considering weak friendship ties among university-affiliated persons in a large-scale online social network. Additionally, the authors explore institutional covariates, such as prestige and public or private status, as moderators of the relationship between distance and social tie probability. The findings demonstrate that geographical distance continues to affect social ties, despite the absence of physical barriers to tie formation and maintenance. The authors find moreover that institutional factors differentially affect the propensity for two university-affiliated individuals to be tied across large distances, illustrating that systematic differences in network structure along status lines persist even in ostensibly unconstrained settings.