Online Social Network Datasets Research Articles

A LOCALLY AND GLOBALLY TUNED METAHEURISTIC OPTIMIZATION FOR OVERLAPPING COMMUNITY DETECTION

Many people use online social networks to share their opinions and information in this digital age. The number of people engaged and their dynamic nature pose a major challenge for social network analysis (SNA). Community detection is one of the most critical and fascinating issues in social network analysis. Researchers frequently employ node features and topological structures to recognize important and meaningful performance in order to locate non-overlapping communities. We introduce a locally and globally tuned multi-objective biogeography-based optimization (LGMBBO) technique in this research for detecting overlapping communities based on the number of connections and node similarity. Four real- world online social network datasets were used in the experiment to assess the quality of both overlapping and non-overlapping partitions. As a result, the model generates a set of solutions that have the best topological structure of a network with node properties. The suggested model will increase their productivity and enhance their abilities to identify significant and pertinent communities.

HAkAu: hybrid algorithm for effective k-automorphism anonymization of social networks

Online social network datasets contain a large amount of various information about their users. Preserving users’ privacy while publishing or sharing datasets with third parties has become a challenging problem. The k-automorphism is the anonymization method that protects the social network dataset against any passive structural attack. It provides a higher level of protection than other k-anonymity methods, including k-degree or k-neighborhood techniques. In this paper, we propose a hybrid algorithm that effectively modifies the social network to the k-automorphism one. The proposed algorithm is based on the structure of the previously published k-automorphism KM algorithm. However, it solves the NP-hard subtask of finding isomorphic graph extensions with a genetic algorithm and employs the GraMi algorithm for finding frequent subgraphs. In the design of the genetic algorithm, we introduce the novel chromosome representation in which the length of the chromosome is independent of the size of the input network, and each individual in each generation leads to the k-automorphism solution. Moreover, we present a heuristic method for selecting the set of vertex disjoint subgraphs. To test the algorithm, we run experiments on a set of real social networks and use the SecGraph tool to evaluate our results in terms of protection against deanonymization attacks and preserving data utility. It makes our experimental results comparable with any future research.

Toward pragmatic modeling of privacy information propagation in online social networks

The growing popularity of online social networks (OSNs) in recent years has generated a lot of concern on personal privacy. One approach of protecting privacy in OSNs is to intervene in the flow of privacy information, making the study of the dynamics of privacy information propagation necessary for the design of effective privacy protection mechanisms. Although previous work on information propagation has produced some models, these models are not adequate for privacy information since they do not reflect the main characteristics of privacy information. In this paper, we propose a model for privacy information propagation. We first analyze the structural characteristics of privacy information and then design the model by incorporating these characteristics. A unique feature of the model is that it infers the privacy attitudes of the information recipients to the privacy concerning subject implicated in the privacy information to determine the forwarding decisions of the recipients. Thus, by mapping the heterogeneous tendency of information forwarding by the recipients to a limited number of privacy attitudes, the model can predict the decisions on forwarding privacy information and thus describe the macroscopic process of privacy information propagation. Results of the experiment based on real OSN datasets show that the proposed model can be used to learn both the scope and the trend of privacy information propagation in OSNs, demonstrating the importance of the privacy attitudes of recipients on privacy information propagation. The properties of the model are also studied through experiment to examine the impact of various factors on privacy information propagation in OSNs.

Effect of Disinformation Propagation on Opinion Dynamics: A Game Theoretic Approach

Disinformation can alter or manipulate our values, opinions, and rational decisions toward any life event because disinformation, such as fake news or rumors, is propagated rapidly and broadly in online social networks (OSNs). Game-theoretic models can help people maximize the benefits from dynamic social interactions. This work presents an opinion framework formulated by repeated, incomplete information games that model OSN users’ subjective opinions. The users may update their opinions using various criteria, such as uncertainty, homophily, encounter, herding, or assertion. We demonstrate how Subjective Logic, a belief model explicitly handling opinion uncertainty, can be employed to model attackers’ deception strategies, users’ opinion update models, and the influences of propagating disinformation through the interactions between users. Through extensive experiments, we investigated how an individual user's information processing type can introduce different impacts on the extent of disinformation propagation. We compared the performance of the five different opinion update models under OSNs characterized by two real OSN datasets. We analyzed their impact on the choices of best strategies, their utilities, and network/opinion polarization. We also examined how the player's choices of best strategies under uncertainty are different from Nash Equilibrium strategies based on correct beliefs towards their opponents’ moves.

RLIM: representation learning method for influence maximization in social networks

A core issue in influence propagation is influence maximization, which aims to find a set of nodes that maximize influence spread by adopting a specific information diffusion model. The limitation of the existing algorithms is they excessively depend on the information diffusion model and randomly set the propagation ability. Therefore, most algorithms are difficult to apply in large-scale social networks. A method to solve the problem is neural network architecture. Based on the architecture, the paper proposes Representation Learning for Influence Maximization (RLIM) algorithm. The algorithm consists of three main parts: the influence cascade of each source node is the premise; the multi-task deep learning neural network to classify influenced nodes and predict propagation ability is the fundamental bridge; the prediction model applying to the influence maximization problem by the greedy strategy is the purpose. Furthermore, the experimental results show that the RLIM algorithm has greater influence spread than the state-of-the-art algorithms in different online social network datasets, and the information diffusion is more accurate.

Public perception of electric vehicles on reddit over the past decade

Understanding public perception of electric vehicles (EVs) is imperative for increasing EV adoption, which can significantly reduce greenhouse gas (GHG) emissions, thereby mitigating climate change and global warming. While most existing research characterizes public perception of EV in the context of surveys, questionnaires, or interviews, our study leverages Reddit online social network (OSN) data to capture EV public perception at a much larger scale. We have collected 3,437,917 Reddit posts (including 274,979 submissions and 3,162,938 comments) between January 2011 and December 2020 relevant to EVs and analyzed them along several axes to understand how EVs are perceived by the public on Reddit through the following research questions: (1) What EV-related topics have been discussed by Reddit users? Whether/how Reddit users’ interest in different topics has changed during 2011–2020? (2) What sentiment do Reddit users hold towards EVs? Whether public sentiment on Reddit has shifted over the past 10 years? (3) Whether/how do various Reddit communities (i.e., subreddits) have different perceptions of EVs? Our analysis evinces the potential of utilizing a large-scale OSN dataset for demonstrating a much wider spectrum of topics that the public is interested in than previous studies show, reveals fringe communities including r/conspiracy have many (controversial) discussions on EVs, especially on the environmental impacts of EVs, and one political community (r/The_Donald) has similar patterns with fringe communities in both sentiment and topic aspects. By answering these research questions, we aim to develop a more comprehensive understanding of the public perception of EVs in the past decade.

User Trust Inference in Online Social Networks: A Message Passing Perspective

Online social networks are vital environments for information sharing and user interactivity. To help users of online social services to build, expand, and maintain their friend networks or webs of trust, trust management systems have been deployed and trust inference (or more generally, friend recommendation) techniques have been studied in many online social networks. However, there are some challenging issues obstructing the real-world trust inference tasks. Using only explicit yet sparse trust relationships to predict user trust is inefficient in large online social networks. In the age of privacy-respecting Internet, certain types of user data may be unavailable, and thus existing models for trust inference may be less accurate or even defunct. Although some less interpretable models may achieve better performance in trust prediction, the interpretability of the models may prevent them from being adopted or improved for making relevant informed decisions. To tackle these problems, we propose a probabilistic graphical model for trust inference in online social networks in this paper. The proposed model is built upon the skeleton of explicit trust relationships (the web of trust) and embeds various types of available user data as comprehensively-designed trust-aware features. A message passing algorithm, loop belief propagation, is applied to the model inference, which greatly improves the interpretability of the proposed model. The performance of the proposed model is demonstrated by experiments on a real-world online social network dataset. Experimental results show the proposed model achieves acceptable accuracy with both fully and partially available data. Comparison experiments were conducted, and the results show the proposed model’s promise for trust inference in some circumstances.

Continuous Influence-Based Community Partition for Social Networks

Community partition is of great importance in social networks because of the rapid increasing network scale, data and applications. We consider the community partition problem under LT model in social networks, which is a combinatorial optimization problem that divides the social network to disjoint $m$ communities. Our goal is to maximize the sum of influence propagation through maximizing it within each community. As the influence propagation function of community partition problem is supermodular under LT model, we use the method of Lov{$\acute{a}$}sz Extension to relax the target influence function and transfer our goal to maximize the relaxed function over a matroid polytope. Next, we propose a continuous greedy algorithm using the properties of the relaxed function to solve our problem, which needs to be discretized in concrete implementation. Then, random rounding technique is used to convert the fractional solution to integer solution. We present a theoretical analysis with $1-1/e$ approximation ratio for the proposed algorithms. Extensive experiments are conducted to evaluate the performance of the proposed continuous greedy algorithms on real-world online social networks datasets and the results demonstrate that continuous community partition method can improve influence spread and accuracy of the community partition effectively.

Influence Maximization Based on Snapshot Prediction in Dynamic Online Social Networks

With the vigorous development of the mobile Internet, online social networks have greatly changed the way of life of human beings. As an important branch of online social network research, influence maximization refers to finding K nodes in the network to form the most influential seed set, which is an abstract model of viral marketing. Most of the current research is based on static network structures, ignoring the important feature of network structures changing with time, which discounts the effect of seed nodes in dynamic online social networks. To address this problem in dynamic online social networks, we propose a novel framework called Influence Maximization based on Prediction and Replacement (IMPR). This framework first uses historical network snapshot information to predict the upcoming network snapshot and then mines seed nodes suitable for the dynamic network based on the predicted result. To improve the computational efficiency, the framework also adopts a fast replacement algorithm to solve the seed nodes between different snapshots. The scheme we adopted exhibits four advantages. First, we extended the classic influence maximization problem to dynamic online social networks and give a formal definition of the problem. Second, a new framework was proposed for this problem and a proof of the solution is given in theory. Third, other classical algorithms for influence maximization can be embedded into our framework to improve accuracy. More importantly, to reveal the performance of the scheme, a series of experiments based on different settings on real dynamic online social network datasets were carried out, and the experimental results are very promising.

Diversity Preference-Aware Link Recommendation for Online Social Networks

Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications. Existing link recommendation methods tend to recommend similar friends to a user but overlook the user's diversity preference, although social psychology theories suggest the criticality of diversity preference to link recommendation performance. In recommender systems, a field related to link recommendation, a number of diversification methods have been proposed to improve the diversity of recommended items. Nevertheless, diversity preference is distinct from diversity studied by diversification methods. To address these research gaps, we define and operationalize the concept of diversity preference for link recommendation and propose a new link recommendation problem: the diversity preference-aware link recommendation problem. We then analyze key properties of the new link recommendation problem and develop a novel link recommendation method to solve the problem. Using two large-scale online social network data sets, we conduct extensive empirical evaluations to demonstrate the superior performance of our method over representative diversification methods adapted for link recommendation as well as state-of-the-art link recommendation methods.

ELM-NeuralWalk: trust evaluation for online social networks

Trust relationship plays an important role in online shopping, recommendation systems, internet of things, etc. The problem of trust evaluation among users in online social network (OSN) has attracted much attention, and has become a hot issue in the domain of social computing. However, the way of trust propagation and aggregation in OSN is still not clear, as well as the accuracy of trust calculation. In order to calculate the indirect trust, an ELM-NeuralWalk algorithm to implement trust propagation and aggregation is proposed. ELM-WalkNet firstly learns two-hop trust calculation rules, then calculates two-hop trust among users in the OSN. After that, ELM-NeuralWalk updates the OSN with the calculated trust value, so as to realise the calculation of multi-hop trust among users through iterative calling ELM-WalkNet. Unlike traditional solutions that use inference methods, ELM-WalkNet can learn trust calculation rules in an inductive way and accurately calculate indirect trust between users. Experiments on two real OSN datasets showed that ELM-NeuralWalk outperforms existing solutions.

Influence Maximization Based on Backward Reasoning in Online Social Networks

Along with the rapid development of information technology, online social networks have become more and more popular, which has greatly changed the way of information diffusion. Influence maximization is one of the hot research issues in online social network analysis. It refers to mining the most influential top-K nodes from an online social network to maximize the final propagation of influence in the network. The existing studies have shown that the greedy algorithms can obtain a highly accurate result, but its calculation is time-consuming. Although heuristic algorithms can improve efficiency, it is at the expense of accuracy. To balance the contradiction between calculation accuracy and efficiency, we propose a new framework based on backward reasoning called Influence Maximization Based on Backward Reasoning. This new framework uses the maximum influence area in the network to reversely infer the most likely seed nodes, which is based on maximum likelihood estimation. The scheme we adopted demonstrates four strengths. First, it achieves a balance between the accuracy of the result and efficiency. Second, it defines the influence cardinality of the node based on the information diffusion process and the network topology structure, which guarantees the accuracy of the algorithm. Third, the calculation method based on message-passing greatly reduces the computational complexity. More importantly, we applied the proposed framework to different types of real online social network datasets and conducted a series of experiments with different specifications and settings to verify the advantages of the algorithm. The results of the experiments are very promising.

Social influence‐based privacy inference attacks in online social networks

AbstractPrivacy inference attacks in online social networks aim at inferring hidden attributes of target users based on released attributes of the users as well as information about social relationships in the networks. Although many privacy inference methods have been proposed in recent years, most of the methods have mostly focused on pursuing high accuracy of the inference results without paying too much attention on identifying users who make the most contributions to the inference, making it less useful in the development of effective countermeasures. In this paper, by taking full consideration of the roles of users in privacy inference, we propose a privacy inference method based on graph attention networks that can provide information about not only the private attributes of the target users, but also the importance of the users in the process of inferring the results. We also design a novel mechanism for the selection of influential users to make our proposed method adaptive to the characteristics of user influence diffusion, resulting in involving fewer number of users in privacy inference and hence improving the efficiency. We also performed some experiment to evaluate the proposed method using a real online social network dataset to show the effectiveness of the proposed method. Comparison with other comparable methods also shows that the results of our proposed method are more accurate.

An Information Diffusion Model Based on Explosion Shock Wave Theory on Online Social Networks

Along with the growing popularity of online social networks, an environment has been set up with information spreading faster and wider than ever before, which has changed the way of information diffusion. Previous empirical research and propagation models have been conducted to illustrate how information propagates on online social networks. However, due to the complexity of information diffusion, there are still many important issues yet to be resolved. In order to tackle this problem, most studies have assumed that information is transmitted along the edges on online social networks, while most research goals aim to discover nodes that have been affected by information diffusion. However, we found that processes of information diffusion on online social networks vary from one another; some topics such as people’s livelihood and education are long-acting while some entertainment news is short-lived. The scale of propagation may be similar in the end, but the spreading process would be completely different. With the purpose of modeling the propagation process more realistically, we propose a novel model, the Information Diffusion Model, based on Explosion Shock Wave Theory. The Information Diffusion Model compares the propagation process to the explosion of an information bomb at the source, with the information shock waves progressively spread from near to far. Additionally, we establish rules of information transmission between a pair of individuals. The approach we adopted demonstrates four strengths. First, it models information diffusion on OSNs considering the differences between individuals and individual social behaviors, which takes the individual background knowledge and forgetting factors into account. Second, it holds the point that the attractiveness of information to individuals is related to the value of information. Third, it recognizes the role of community in the diffusion process; with a higher sense of trust established in a community, the spread of information would be more convenient. More importantly, the model we put forth is applicable to different types of real online social network datasets. Many experiments with different settings and specifications are conducted to verify the advantages of the model, and the results obtained are very promising.

Facebook Tells Me Your Gender: An Exploratory Study of Gender Prediction for Turkish Facebook Users

Online Social Networks (OSNs) are very popular platforms for social interaction. Data posted publicly over OSNs pose various threats against the individual privacy of OSN users. Adversaries can try to predict private attribute values, such as gender, as well as links/connections. Quantifying an adversary’s capacity in inferring the gender of an OSN user is an important first step towards privacy protection. Numerous studies have been made on the problem of predicting the gender of an author/user, especially in the context of the English language. Conversely, studies in this field are quite limited for the Turkish language and specifically in the domain of OSNs. Previous studies for gender prediction of Turkish OSN users have mostly been performed by using the content of tweets and Facebook comments. In this article, we propose using various features, not just user comments, for the gender prediction problem over the Facebook OSN. Unlike existing studies, we exploited features extracted from profile, wall content, and network structure, as well as wall interactions of the user. Therefore, our study differs from the existing work in the broadness of the features considered, machine learning and deep learning methods applied, and the size of the OSN dataset used in the experimental evaluation. Our results indicate that basic profile information provides better results; moreover, using this information together with wall interactions improves prediction quality. We measured the best accuracy value as 0.982, which was obtained by combining profile data and wall interactions of Turkish OSN users. In the wall interactions model, we introduced 34 different features that provide better results than the existing content-based studies for Turkish.

Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rules

A malicious data miner can infer users’ private information in online social networks (OSNs) by data mining the users’ disclosed information. By exploring the public information about a target user (i.e. an individual or a group of OSN users whose privacy is under attack), attackers can prepare a training data set holding similar information about other users who openly disclosed their data. Using a machine learning classifier, the attacker can input released information about users under attack as non-class attributes and extract the private information as a class attribute. Some techniques offer some privacy protection against specific classifiers;, however, the provided privacy can be under threat if an attacker uses a different classifier (rather than the one used by the privacy protection techniques) to infer sensitive information. In reality, it is difficult to predict the classifiers involved in a privacy attack. In this study, we propose a privacy-preserving technique which first prepares a training data set in a similar way that an attacker can prepare and then takes an approach independent of the classifiers to extract patterns (or logic rules) from the training data set. Based on the extracted rule set, it then suggests the target users to hide some non-class attribute values and/or modify some friendship links for protecting their privacy. We apply our proposed technique on two OSN data sets containing users’ attribute values and their friendship links. For evaluating the performance of the proposed technique, we use conventional classifiers such as Na $$\ddot{\text {i}}$$ ve Bayes, Support Vector Machine and Random Forest on the privacy-protected data sets. The experimental results show that our proposed technique outperforms the existing privacy-preserving algorithms in terms of securing privacy while maintaining the data utility.

SIoT: A New Strategy to Improve the Network Lifetime with an Efficient Search Process

The Social Internet of Things (SIoT) means that every node can use a set of nodes that are considered as friends to search for a specific service. However, this is a slow process because each node is required to manage a high number of friends. Thus, the SIoT issue consists of how to select the right friends that improve the network navigability. The enhancement of the network navigability boosts the search for a service to be rapid but not guaranteed. Furthermore, sending requests from the shortest paths involves the rapid search, but the network lifetime can be reduced due to the number of requests that can be transmitted and processed by the nodes that have low power energy. This paper proposes a new approach that improves the network navigability, speeds up the search process, and increases the network lifetime. This approach aims at creating groups dynamically by nodes where each group has a master node, second, using a consensus algorithm between master nodes to agree with a specific capability, finally adopting a friendship selection method to create a social network. Thus, the friends will be sorted periodically for the objective of creating simultaneously a balance between the energy consumption and the rapid search process. Simulation results on the Brightkite location-based online social network dataset demonstrate that our proposal outperforms baseline methods in terms of some parameters of network navigability, path length to reach the providers, and network lifetime.

Discount allocation for cost minimization in online social networks

We introduce the discount allocation problem to a new online social networks (OSNs) scenario where the nodes and the relationships between nodes are determined but the states of edges between nodes are unknown. We can know the states of all the edges centered on a node only when it becomes active. Different from most previous work on influence maximization discount allocation problem in OSNs, our goal is to minimize the discount cost that the marketer spends while ensuring at least Q customers who adopt the target product in the end in OSNs. We propose an online discount allocation policy to select seed users to spread the product information. The marketer initially selects one seed user to offer him a discount and observes whether he accepts the discount. If he accepts the discount, the marketer needs to observe how well this seed user contributes to the diffusion of product adoptions and how much discount he accepts. The remaining seeds are chosen based on the feedback of diffusion results obtained by all previous selected seeds. We propose two online discount allocation greedy algorithms under two different situations: uniform and non-uniform discounts allocation. We offer selected users discounts changing from the lowest to highest in the discount rate set until the users receive the discount and become seed users in non-uniform discount allocation situation, which saves the cost of firms comparing with the previous method that providing product to users for free. We present a theoretical analysis with bounded approximation ratios for the algorithms. Extensive experiments are conducted to evaluate the performance of the proposed online discount allocation algorithms on real-world online social networks datasets and the results demonstrate the effectiveness and efficiency of our methods.

Influence Diffusion in Online Social Networks With Propagation Rate Changes

Information can propagate among Online Social Network (OSN) users at a high speed, which makes the OSNs important platforms for viral marketing. Although the viral marketing related problems in OSNs have been extensively studied in the past decade, the existing works all assume known propagation rates. In this paper, we propose a novel model, Dynamic Influence Propagation (DIP), which allows propagation rates to increase after a topic becomes popular and can be used for describing information propagation in OSNs more realistically. Based on DIP, we define a new research problem: Threshold Activation Problem under DIP (TAP-DIP). However, it adds another layer of complexity over the already #P-hard TAP problem. Despite it hardness, we are able to approximate TAP-DIP with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\log |V|)$</tex-math></inline-formula> ratio, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$|V|$</tex-math></inline-formula> is the number of users in the network. Our solution consists of global optimization techniques and a novel solution to the general version of TAP. We also consider the more complicated case when the propagation rates may change multiple times and the changes are non-immediate, with corresponding solution and analyses. We test our solution using various real OSN datasets, and demonstrate that our solution not only generates high-quality seed sets, but also scales.

Pricing influential nodes in online social networks

Influential nodes with rich connections in online social networks (OSNs) are of great values to initiate marketing campaigns. However, the potential influence spread that can be generated by these influential nodes is hidden behind the structures of OSNs, which are often held by OSN providers and unavailable to advertisers for privacy concerns. A social advertising model known as influencer marketing is to have OSN providers offer and price candidate nodes for advertisers to purchase for seeding marketing campaigns. In this setting, a reasonable price profile for the candidate nodes should effectively reflect the expected influence gain they can bring in a marketing campaign. In this paper, we study the problem of pricing the influential nodes based on their expected influence spread to help advertisers select the initiators of marketing campaigns without the knowledge of OSN structures. We design a function characterizing the divergence between the price and the expected influence of the initiator sets. We formulate the problem to minimize the divergence and derive an optimal price profile. An advanced algorithm is developed to estimate the price profile with accuracy guarantees. Experiments with real OSN datasets show that our pricing algorithm can significantly outperform other baselines.