Social Network Datasets Research Articles

An effective scheme to address influence maximization for opinion formation in social networks

AbstractInfluence maximization for opinion formation (IMOF) is an important problem in social networks, which aims to select most influential nodes and obtains the maximal propagation of the most ideal opinions. The existing studies on the IMOF primarily concentrate on the effective selection scheme of the most influential nodes (ie, seed nodes) and the improved opinion formation models. However, there is little work describing and defining the IMOF mathematically. In this paper, we formulate the IMOF problem mathematically and propose the weighted cooperation model to compute the opinions of network nodes. To determine the most influential nodes, an effective scheme (including the score of potential nodes (SPNs) and elimination of overlapping influence (EOI)) is proposed. By using the reverse propagation process of public opinion and dynamically adjusting the influence score of each node, SPN determines the sorting of network node within the finite iterations. Moreover, to better determine the initial seed nodes, the EOI is adopted to deal with the similarity and overlapping influence among these nodes with high influence score. Finally, to evaluate the performance of SPN, the number of positive nodes with the change of seed nodes and the times of iteration by SPN is compared with three mainstream algorithms in social network data sets. Simulation results show that the proposed scheme obtains superior number of positive nodes than the comparison algorithms.

Retrieving Hidden Friends: A Collusion Privacy Attack Against Online Friend Search Engine

Online social networks (OSNs) are providing a variety of applications for human users to interact with families, friends, and even strangers. One such application, the friend search engine, allows the general public to query individual users’ friend lists and has been gaining popularity recently. However, without proper design, this application may mistakenly disclose users’ private relationship information. Our previous work has proposed a privacy preservation solution that can effectively boost OSNs’ sociability while protecting users’ friendship privacy against attacks launched by individual malicious requestors. In this paper, we propose an advanced collusion attack, where a victim user’s friendship privacy can be compromised through a series of carefully designed queries coordinately launched by multiple malicious requestors. The effect of the proposed collusion attack is validated through synthetic and real-world social network data sets. The in-depth research on the advanced collusion attacks will help us design a more robust and secure friend search engine on OSNs in the near future.

Querying relational event graphs using colored range searching data structures

We present a general approach for analyzing structural parameters of a relational event graph within arbitrary query time intervals using colored range query data structures. Relational event graphs generally represent social network datasets, where each graph edge carries a timestamp. We provide data structures based on colored range searching to efficiently compute several graph parameters (e.g., density, neighborhood overlap, h-index).

A fast algorithm on generating concept lattice for symmetry formal context constructed from social networks

Formal concept analysis has been witnessed to be an effective soft computing methodology for data analysis, rule extraction and clustering, but how to build a formal concept lattice efficiently is always a challenge issue. Recent years, several deformation formal contexts are emerging for addressing current important research problems, such as virtual machines scheduling in mobile cloud computing and topological structure analysis in social networks. In this paper, we focus on the symmetry formal context where the objects and attributes are identical, and its formal concept lattice construction. Firstly, we explore the properties of symmetry formal context and discover a symmetry line which must appear in the expected formal concept lattice. Further, a fast algorithm for building concept lattice for symmetry formal context is presented. Then, five social network datasets are utilized for evaluating our proposed algorithm. Experimental results show that our algorithm can speed up 1.2 times and 3.45 times compared to the concept-matrix based concepts generation algorithm and incremental algorithm, respectively.

Dynamic Multi-layer Ensemble Classification Framework for Social Venues Using Binary Particle Swarm Optimization

Multi-layer ensemble frameworks perform much better as compared to individual classifiers. However, selection of a classifier and its placement, impacts the overall performance of ensemble framework. This problem becomes very difficult, if there are more classifiers and layers. To address these problems in this paper, we design “Binary Particle Swarm Optimization” method for selection and placement of right classifiers in multi-layer ensemble model. Proposed classifier weight-assignment method is implemented to prioritize the selected classifiers. The model is simulated for the classification of social-user check-ins in Location-Based Social Network datasets. The experimental results show that the proposed ensemble model outperforms the state-of-the-art ensemble methods in the literature. It can be used by security firms, high level decision makers and various governmental organizations for tracking malicious users.

Proactively Placing Static Relays With Social-Link Awareness in Mobile Social Networks

Mobile social networks have been exploited for data forwarding due to its low cost and better robustness. Existing data forwarding strategies in mobile social networks rely on the pairwise contacts among mobile users. However, these pairwise contacts only provide limited forwarding capabilities and most data fail to be delivered before the expiration time. In this paper, we improve the performance of data forwarding by proactively placing low-cost static relays to increase the opportunistic contacts among the mobile nodes. Based on this idea, an important question is where to place the static relays in the network to best facilitate data forwarding among nodes. To answer this question, we first analyze four real-world datasets of mobile social networks, and identify that data forwarding only appears in a small group of social links. Then, we formalize the problem of static relay placement as an optimization problem, and propose a heuristic based solution to improve the data forwarding performance along these social links. Considering the fact that the social links may evolve over time, we also propose an efficient relay replacement algorithm which replaces the outdated relays with new relays to further improve the data forwarding performance. Evaluation results show that the proposed relay based solutions can significantly improve the performance of data forwarding in mobile social networks.

ComBIM: A community-based solution approach for the Budgeted Influence Maximization Problem

Given a social network of users with non-uniform selection cost, and a fixed budget, which nodes should be chosen for initial activation to maximize the influence in the network, such that the total selection cost does not exceed the budget? This problem is known as the Budgeted Influence Maximization Problem or BIM Problem, in short. Though the problem appears to be realistic one, there are very few studies available in the literature. In this paper, we propose ComBIM, a community-based solution approach for solving the BIM problem. The proposed methodology has four steps: first, community detection to understand the inherent structure of the social network, second, budget distribution to divide the total budget among the communities based on the number of nodes and their selection costs, third, seed selection for influence maximization and finally, budget transfer, in which unutilized budget of one community is transferred to another community. We implement the proposed methodology with three publicly available social network datasets. We have compared the obtained results with that of other methodologies from the literature and observe that ComBIM can achieve the better influence spread, while taking reasonable time for seed set selection.

Detecting sequences of system states in temporal networks

Many time-evolving systems in nature, society and technology leave traces of the interactions within them. These interactions form temporal networks that reflect the states of the systems. In this work, we pursue a coarse-grained description of these systems by proposing a method to assign discrete states to the systems and inferring the sequence of such states from the data. Such states could, for example, correspond to a mental state (as inferred from neuroimaging data) or the operational state of an organization (as inferred by interpersonal communication). Our method combines a graph distance measure and hierarchical clustering. Using several empirical data sets of social temporal networks, we show that our method is capable of inferring the system’s states such as distinct activities in a school and a weekday state as opposed to a weekend state. We expect the methods to be equally useful in other settings such as temporally varying protein interactions, ecological interspecific interactions, functional connectivity in the brain and adaptive social networks.

Social context-aware trust paths finding for trustworthy service provider selection in social media

Online Social Network (OSN) has been used to enhance service provision and service selection, where trust is one of the most important factors for the decision making of service consumers. Thus, a significant and challenging problem is how to effectively and efficiently find those social trust paths that can yield trustworthy trust evaluation results based on the requirements of a service consumer particularly in contextual OSNs which contains social contexts, like social relationships and social trust between participants, and social positions of participants. In this paper, we propose a new concept called Strong Social Graph (SSG), consisting of participants with strong social connections. We also propose an approach to identify SSGs, and propose a novel index method and a graph compression method for SSG. Then based on the compressed SSG and indices, we propose a new efficient and effective approximation algorithm, called SSG-MCBA by adopting the Monte Carlo method and our optimization search strategies. The experiments conducted onto two real social network datasets illustrate that SSG-MCBA greatly outperforms the state-of-the-art method in both efficiency and effectiveness.

Uncovering the relationship between point-of-interests-related human mobility and socioeconomic status

In a city or region, understanding the relationship between human mobility and socioeconomic status is critical to public policies formulation, urban design and marketing strategies development. Based on the available massive geo-located human data, existing studies focused almost exclusively on the position attributes (i.e. coordinates) of the locations visited by people to explore the relationship, however, they ignored the category attributes (e.g. restaurant or supermarket) of these locations which imply the purposes (e.g. eating or shopping) behind human movements. A location with coordinates and category information is usually referred to as a point-of-interest (POI). In this paper, we study the relationship between POIs-related human mobility and socioeconomic status at city level. Starting from the location-based social network (i.e. Foursquare) dataset, we find that the check-in numbers of location categories are correlated with socioeconomic indicators, either positively or negatively. To further validate these correlations, we develop and test a multi-task prediction framework based on POIs-related human mobility for forecasting socioeconomic indicators. Extensive experiments on the Foursquare dataset show that the socioeconomic indicators can be well predicted by our proposed framework. Our findings and methods are helpful for modeling human mobility and assessing socioeconomic status.

Improved Link Prediction Using PCA

Link Prediction is known as a challenging problem in the area of online social media. Earlier, learning model for link prediction task has been proposed by many researchers. But the classification of imbalanced and high dimensional data is an interesting and challenging problem in machine learning due to presence of unbalanced and redundant or correlated data which break down the classification performance. In this paper, we have balanced the data and used Principle Component Analysis (PCA) to reduce the correlated data and improved the performance of link prediction model. Experiment is carried out on social network data set and the use of PCA method has improved the performance in classification of links.

A Unified Semantic Model for Cross-Media Events Analysis in Online Social Networks

The gap between the raw data from various data sources and the diverse intelligent applications has been an obstacle in the field of events analysis in online social networks. Most existing analysis systems focus on data from a certain single online social network platform and a limited range of analysis applications. To comprehensively understand events, the sources of data usually include multiple online social network platforms and different existing corpora. Thus, it is necessary to build a bridge to handle the online social data from different sources and support various analysis application requirements. In this paper, a unified semantic model for events analysis is proposed. The model contains well-designed classes and properties to tackle the lack of unified representation, and provenance information is also taken into consideration. The reasoning is supported to check the consistency of the data and to discover hidden knowledge such as tacit classification and implicit relationships. The schema mapping and data transformation methods are provided to handle the heterogeneous data from various online social network platforms and datasets. The design of the cross-media event analysis system is also presented. The comparison shows the advantages of this paper. The case study shows the applicability and effectiveness of our model and system.

Structural Representation Learning for User Alignment Across Social Networks

Aligning users across different social networks has become increasingly studied as an important task to social network analysis. In this paper, we propose a novel representation learning method that mainly exploits social structures for the network alignment. In particular, the proposed network embedding framework models the follower-ship and followee-ship of each user explicitly as input and output context vectors, while preserving the proximity of users with “similar” followers and followees in the embedded space. We incorporate both known and predicted user anchors across the networks as constraints to facilitate the transfer of context information to achieve accurate user alignment. Both network embedding and user alignment are inferred under a unified optimization framework with negative sampling adopted to ensure scalability. Also, variants of the proposed framework, including the incorporation of higher-order structural features, are also explored for further boosting the alignment accuracy. Extensive experiments on large-scale social and academia network datasets demonstrate the efficacy of our proposed model compared with state-of-the-art methods.

ABNE: An Attention-Based Network Embedding for User Alignment Across Social Networks

User alignment across social networks can facilitate more information/knowledge transferring across networks and thereby benefit several applications, including social link prediction, cross-domain recommendation, and information diffusion. Several works try to learn a common subspace for networks by preserving the structural proximities, such that different contribution weights of neighbors are ignored as users were always connected by unweighted edges. In this paper, we propose an attention-based network embedding model that exploits the social structures for user alignment. In particular, two main components are contained in our model framework: a masked graph attention mechanism which tries to learn the alignment task driven attention weights by the supervision of pre-aligned user pairs, and an embedding algorithm tries to learn a common vector space by explicitly modeling the weighted contribution probabilities between follower-ships and followee-ships. With the learned weights and embeddings transferring between these two components, we construct a unified model for user embedding and alignment. Stochastic gradient descent and negative sampling are adopted for efficient learning and scalability. The extensive experiments on real-world social network data sets demonstrate the effectiveness and robustness of the proposed model compared with several state-of-the-art methods.

Distributed and personalised social network privacy protection

Considering the privacy issues on social network, a variety of anonymous techniques have been proposed, but these techniques neglect some differences among individuals in their demand for privacy protection. With the development of internet technology, the number of social network individuals increases yearly, and network data are poised for a massive change in trends. Motivated by this, we specify three levels of privacy information for victim individuals and propose a personalised k-degree-m-label (PKDML) anonymity model. Furthermore, we design and implement a distributed and personalised k-degree-m-lable (DPKDML) anonymisation algorithm, which takes advantage of the 'vertex-centric' GraphX programming model to complete the entire anonymous process by multiple message passing and node value updating. Finally, we conduct experiments on real social network datasets to evaluate the DPKDML, The experimental results show that our methods may overcome the shortcomings of traditional methods in processing massive data, and reduce anonymous costs and increase data utility.

Integrating Social Links into Wireless Networks: Modeling, Routing, Analysis, and Evaluation

Social connections among network nodes have been well investigated as an additional opportunity in network design (e.g., in routing strategies and trusted networking). This paper presents a paradigm shift that explores the design and performance analysis of combining social links jointly with communication links for message delivery in wireless networks. In a combined multi-layer social and communication network, communication links are based on conventional wireless technologies (e.g., WiFi, Bluetooth) and social links are overlaid over a communication infrastructure (e.g., cellular network) that provides an alternative way for data transmission. The goal is to characterize the performance analytically when routing is designed by combining social and communication links. A distance discretization technique is applied to model the reliability and delay of message delivery. The analytical foundation is developed to analyze the end-to-end delay and success probability under various effects of persistent transmission, potential error in distance estimation, and mobility. Systematic routing strategies that employ network inference are then designed to improve the performance in different aspects, such as delivery delay, delivery success probability, and energy-saving. A network emulation testbed is implemented with actual radios and real-world social network datasets to measure the performance of a heterogeneous network with social and communication links. The results in this paper show that the integration of social links in wireless network routing as a multi-layer design leads to substantial performance improvement for delay and reliability of message delivery.

Influence Maximization in Signed Social Networks With Opinion Formation

Influence maximization (IM) has been widely studied in recent years. Given fixed number of seed users and certain diffusion models, the IM problem aims to select proper seed users in a social networks such that they can achieve the maximal spread of influence. Most previous work assumes that there are only positive relationships between users, and thus users spread influence positively. However, negative relationships also universally exist in various social networks and are complementary to positive relationships in information diffusion. In this paper, the influence maximization problem is addressed in signed social networks that contain both positive and negative relationships. We propose a novel diffusion model called LT-S and two influence spread functions. The proposed LT-S model extends the classical linear threshold model with opinion formation that incorporates both positive and negative opinions and simulates information diffusion in real-world social networks. The influence spread functions under the LT-S model are neither monotone nor submodular which bring challenges to maximization. The RLP algorithm is proposed to tackle the issue, which is improved from R-Greedy algorithm by incorporating two proposed accelerating techniques, the live-edge based and propagation-path based techniques. The results of the extensive experiments on public real signed social network datasets demonstrate that our algorithm outperforms the baseline algorithms in terms of both efficiency and effectiveness.

ERATO: Trading Noisy Aggregate Statistics over Private Correlated Data

With the commoditization of personal privacy, pricing private data has become an intriguing problem. In this paper, we study noisy aggregate statistics trading from the perspective of a data broker in data markets. We thus propose ERATO, which enables aggr E gate statistics p R icing over priv AT e c O rrelated data. On one hand, ERATO guarantees arbitrage freeness against cunning data consumers. On the other hand, ERATO compensates data owners for their privacy losses using both bottom-up and top-down designs. We further apply ERATO to three practical aggregate statistics, namely weighted sum, probability distribution fitting, and degree distribution, and extensively evaluate their performances on MovieLens dataset, 2009 RECS dataset, and two SNAP large social network datasets, respectively. Our analysis and evaluation results reveal that ERATO well balances utility and privacy, achieves arbitrage freeness, and compensates data owners more fairly than differential privacy based approaches.

NET-EXPO: A Gephi Plugin Towards Social Network Analysis of Network Exposure for Unipartite and Bipartite Graphs.

Social network analysis (SNA) concerns itself in studying network structures in relation to individuals' behavior. Individuals may be influenced by their network members in their behavior, and thus past researchers have developed computational methods that allow us to measure the extent to which individuals are exposed to members with certain behavior within one's social network, and that be correlated with their own behavior. Some of these methods include network exposure model, affiliation exposure model, and decomposed network exposure models. We developed a Gephi plugin that computes and visualizes these various kinds of network exposure models called NET-EXPO. We experimented with NET-EXPO on some social network datasets to demonstrate its pragmatic use in social network research. This plugin has the potential to equip researchers with a tool to compute network exposures in a user friendly way and simplify the process to compute and visualize the network data.

AIM: Activation increment minimization strategy for preventing bad information diffusion in OSNs

The openness and virtuality of Online Social Networks (OSNs) make it a hotbed of rapid propagation for various kinds of frauds and erroneous information. Ergo, there is an exigent need to find a method that can expeditiously and efficaciously limit the diffusion of misinformation in OSNs. To resolve this issue, this article proposes the utilization of Activation Increment engendered by a node as a criterion to quantify the importance of the node. Even if the propagation probabilities between the nodes are identically tantamount, due to the dynamics of information propagation and high connectivity of the network, the activation probabilities of nodes are different. The Activation Increment describes the sum of activation probabilities of a node’s neighbors while the node itself is in a different state (infected status, recovered status) at a certain time. To utilize Activation Increment, this paper proposes Activation Increment Minimization (AIM) strategy to select and block nodes for information diffusion. Experiments based on the real social network dataset attested that the proposed AIM strategy is superior to the traditional heuristic algorithms.