Large-scale Online Social Networks Research Articles

Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks

Demonstrating compelling causal evidence of the existence and strength of peer-to-peer influence has become the holy grail of modern research in online social networks. In these networks, it has been consistently demonstrated that user characteristics and behavior tend to cluster both in space and in time. There are multiple well-known rival mechanisms that compete to be the explanation for this observed clustering. These range from peer influence to homophily to other unobservable external stimuli. These multiple mechanisms lead to similar observational data, yet have vastly different policy implications. In this paper, we present a novel randomized experiment that tests the existence of causal peer influence in the general population—one that did not involve subject recruitment for experimentation—of a particular large-scale online social network. We utilize a unique social feature to exogenously induce adoption of a paid service among a group of randomly selected users, and in the process develop a clean exogenous randomization of treatment and control groups. A variety of nonparametric, semiparametric, and parametric approaches, ranging from resampling-based inference to ego-level random effects to logistic regression to survival models, yield close to identical, statistically and economically significant estimates of peer influence in the general population of a freemium social network. Our estimates show that peer influence causes more than a 60% increase in odds of buying the service due to the influence coming from an adopting friend. In addition, we find that users with a smaller number of friends experience stronger relative increase in the adoption likelihood due to influence from their peers as compared to the users with a larger number of friends. Our nonparametric resampling procedure-based estimates are helpful in situations of networked data that violate independence assumptions. We establish that peer influence is a powerful force in getting users from free to premium levels, a known challenge in freemium communities.This paper was accepted by Sandra Slaughter, information systems.

Community Extraction Algorithm for Large-Scale Online Social Networks

Community Extraction Algorithm for Large-Scale Online Social Networks

Trust-based privacy-aware participant selection in social participatory sensing

The main idea behind social participatory sensing is to leverage social friends to participate in mobile sensing tasks. A main challenge, however, is the identification and recruitment of sufficient number of well-suited participants. This becomes especially more challenging for large-scale online social networks with unknown network topology and complex friendship relations. Moreover, the potential sparseness of the friendship network may result in insufficient participation, thus reducing the validity of the obtained information. In this paper, we propose a participant selection framework to address the aforementioned limitations. The nomination module of the framework makes use of a customised random surfer to crawl the requester's social graph and identify suitable nominees. The nominee selection is determined as a function of the members' suitability scores and pairwise trust perception among members. The selection module is responsible for selecting the required participants from the set of nominees based on the nominee's timeliness, the number of participants selected so far and the task's remaining time. Moreover, the selection is done in a way that prevents from the formation of a colluding group among the selected participants. Simulation results demonstrate the efficacy of our proposed framework in terms of selecting a large number of reputable participants with high suitability scores, in comparison with state-of-the-art methods.

Security Visualization Analytics Model in Online Social Networks Using Data Mining and Graph-based Structure Algorithms

The rise of the Internet accelerates the creation of various large-scale online social networks, which can be described the relationships and activities between human beings. The online social networks relationships in real world are too big to present with useful information to identify the criminal or cyber-attacks. This research proposed new information security analytic model for online social networks, which called Security Visualization Analytics (SVA) Model. SVA Model used the set of algorithms (1) Graph-based Structure algorithm to analyze the key factors of influencing nodes about density, centrality and the cohesive subgroup to identify the influencing nodes of anomaly and attack patterns (2) Supervised Learning with oneR classification algorithm was used to predict new links from such influencing nodes in online social networks on discovering surprising links in the existing ones of influencing nodes, which nodes in online social networks will be linked next from the attacked influencing nodes to monitor the risk. The results showed 42 influencing nodes of anomaly and attack patterns and can be predict 31 new links from such nodes were achieved by SVA Model with the accuracy of confidence level 95.0%. The new proposed model and results illustrated SVA Model was significance analysis. Such understanding can lead to efficient implementation of tools to links prediction in online social networks. They could be applied as a guide to further investigate of social networks behavior to improve the security model and notify the risk, computer viruses or cyber-attacks for online social networks in advance.

Identification of Active Valuable Nodes in Temporal Online Social Network with Attributes

The rapidly growing online social networks have generated great expectations connected with their potential business values. The aim of this paper is to identify the active valuable nodes that can spread business information to a large fraction of the individuals in large-scale temporal online social networks as quickly as possible. Most studies focus on static social networks, the study on the identification of active valuable nodes in temporal online social networks with quantitative attributes is still young. In this paper, we propose a method to identify active valuable nodes based on their static structural properties and temporal behavioral attributes. The method first chooses the candidates of the active valuable nodes by the static analysis of their structural properties. Then, the candidate's behavioral trend is extracted from its activity records. Through analyzing the spatio-temporal characteristics of the behavioral trend, the method distinguishes active valuable nodes from inactive ones and reveals typical evolutionary processes. We perform experiments on two practical online social networks with thousands of nodes. The experimental results demonstrate that the method can identify the active valuable nodes for information diffusion in large-scale temporal online social networks accurately and efficiently. It would be useful for business applications.

SRSH: A Social Recommender System based on Hadoop

Online Social Networks (OSNs) accumulate a large amount of user-generated data and Social Recommender Systems (SRSs) can help users discover information they are interested. However, most of the existing SRSs do not have good scalabilities to process huge volumes of data. Aiming to this problem we design a social recommender system named SRSH, which is based on Hadoop parallel computing platform. SRSH provides second-degree friends, similar users, user community and content recommendation modules, which can meet user needs of finding potential friends and attractive content. Especially, every core methods existing in these modules above can be implemented using MapReduce parallel programming framework and run in Hadoop cluster. We have conducted extensive related experiments on the realistic dataset and the experimental results show that SRSH scales well and has the ability of dealing with the problem of recommendation in the large-scale OSN.

Understanding Video Sharing Propagation in Social Networks

Modern online social networking has drastically changed the information distribution landscape. Recently, video has become one of the most important types of objects spreading among social networking service users. The sheer and ever-increasing data volume, the broader coverage, and the longer access durations of video objects, however, present significantly more challenges than other types of objects. This article takes an initial step toward understanding the unique characteristics of video sharing propagation in social networks. Based on realworld data traces from a large-scale online social network, we examine the user behavior from diverse aspects and identify different types of users involved in video propagation. We closely investigate the temporal distribution during propagation as well as the typical propagation structures, revealing more details beyond stationary coverage. We further extend the conventional epidemic models to accommodate diverse types of users and their probabilistic viewing and sharing behaviors. The model, effectively capturing the essentials of the propagation process, serves as a valuable basis for such applications as workload synthesis, traffic prediction, and resource provision of video servers.

Visualization of Influencing Nodes in Online Social Networks

The rise of the Internet accelerates the creation of various large-scale online social networks. The online social networks have brought considerable attention as an important medium for the information diffusion model, which can be described the relationships and activities among human beings. The online social networks' relationships in the real world are too big to present with useful information to identify the criminal or cyber attacks. The methodology for information security analysis was proposed with the complementary of Cluster Algorithm and Social Network Analysis, which presented anomaly and cyber attack patterns in online social networks and visualized the influencing nodes of such anomaly and cyber attacks. The closet vertices of influencing nodes could not avoid from the harmfulness in social networking. The new proposed information security analysis methodology and results were significance analysis and could be applied as a guide for further investigate of social network behavior to improve the security model and notify the risk, computer viruses or cyber attacks for online social networks in advance.

MapReduce-Based Large-Scale Online Social Network Worm Simulation

PDF HTML阅读 XML下载 导出引用 引用提醒 基于MapReduce 的大规模在线社交网络蠕虫仿真 DOI: 10.3724/SP.J.1001.2013.04295 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点基础研究发展计划(973)(2012CB315804); 国家自然科学基金(61073179); 国家科技重大专项(2011ZX03002-005-2); 国家高技术研究发展计划(863)(2011AA01A203); 北京市自然科学基金(4122086) MapReduce-Based Large-Scale Online Social Network Worm Simulation Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:利用云计算中的核心技术MapReduce,提出了一种在线社交网络(online social network,简称OSN)蠕虫的仿真方法.为了提高仿真精度,首先提出利用节点属性可调节的OSN 有向图来描述蠕虫传播的各个过程.其次,利用运行在云环境中的多个Map 函数和Reduce 函数来实现对OSN 蠕虫传播各个过程的仿真.在真实的大规模数据集上的仿真实验结果表明,提出的仿真方法不仅具有较强的可扩展性,同时也为相关领域的研究提供了一定的帮助. Abstract:This paper provides an approach for simulating the propagation of online social network worms, based on MapReduce, a key component of the cloud computing. In order to improve the simulation accuracy, the approach describes the phases of the worms’ propagation with OSN directed graph, in which each node owns its tunable attributes. Then, the phases are simulated by different map-functions and reduce-functions, which will finally run in the cloud environment. The experimental results on the real large network datasets show that the simulating approach is scalable and helpful in the research of online social network worms. 参考文献 相似文献 引证文献

Exploiting behaviors of communities of twitter users for link prediction

Currently, online social networks and social media have become increasingly popular showing an exponential growth. This fact have attracted increasing research interest and, in turn, facilitating the emergence of new interdisciplinary research directions, such as social network analysis. In this scenario, link prediction is one of the most important tasks since it deals with the problem of the existence of a future relation among members in a social network. Previous techniques for link prediction were based on structural (or topological) information. Nevertheless, structural information is not enough to achieve a good performance in the link prediction task on large-scale social networks. Thus, the use of additional information, such as interests or behaviors that nodes have into their communities, may improve the link prediction performance. In this paper, we analyze the viability of using a set of simple and non-expensive techniques that combine structural with community information for predicting the existence of future links in a large-scale online social network, such as Twitter. Twitter, a microblogging service, has emerged as a useful source of informative data shared by millions of users whose relationships require no reciprocation. Twitter network was chosen because it is not well understood, mainly due to the occurrence of directed and asymmetric links yet. Experiments show that our proposals can be used efficiently to improve unsupervised and supervised link prediction task in a directed and asymmetric large-scale network.

A Key Issuing Scheme in IBE-based Distributed Online Social Networks

This paper presents a novel key issuing scheme for distributed online social networks based on identity-based encryption (IBE). The scheme involves two essential objects, Key Privacy Authority (KPA) and Privacy Chum (PC). KPA and PC cannot impersonate network users to obtain users’ private keys. The experiment results indicate that the scheme is feasible and efficient, and it can sustain large-scale online social networks.

A Parallel Approach to Link Sign Prediction in Large-Scale Online Social Networks

A Parallel Approach to Link Sign Prediction in Large-Scale Online Social Networks

Emergence of Scale-Free Close-Knit Friendship Structure in Online Social Networks

Although the structural properties of online social networks have attracted much attention, the properties of the close-knit friendship structures remain an important question. Here, we mainly focus on how these mesoscale structures are affected by the local and global structural properties. Analyzing the data of four large-scale online social networks reveals several common structural properties. It is found that not only the local structures given by the indegree, outdegree, and reciprocal degree distributions follow a similar scaling behavior, the mesoscale structures represented by the distributions of close-knit friendship structures also exhibit a similar scaling law. The degree correlation is very weak over a wide range of the degrees. We propose a simple directed network model that captures the observed properties. The model incorporates two mechanisms: reciprocation and preferential attachment. Through rate equation analysis of our model, the local-scale and mesoscale structural properties are derived. In the local-scale, the same scaling behavior of indegree and outdegree distributions stems from indegree and outdegree of nodes both growing as the same function of the introduction time, and the reciprocal degree distribution also shows the same power-law due to the linear relationship between the reciprocal degree and in/outdegree of nodes. In the mesoscale, the distributions of four closed triples representing close-knit friendship structures are found to exhibit identical power-laws, a behavior attributed to the negligible degree correlations. Intriguingly, all the power-law exponents of the distributions in the local-scale and mesoscale depend only on one global parameter, the mean in/outdegree, while both the mean in/outdegree and the reciprocity together determine the ratio of the reciprocal degree of a node to its in/outdegree. Structural properties of numerical simulated networks are analyzed and compared with each of the four real networks. This work helps understand the interplay between structures on different scales in online social networks.

On Modeling Product Advertisement in Large-Scale Online Social Networks

We consider the following advertisement problem in online social networks (OSNs). Given a fixed advertisement investment, e.g., a number of free samples that can be given away to a small number of users, a company needs to determine the probability that users in the OSN will eventually purchase the product. In this paper, we model OSNs as scale-free graphs (either with or without high clustering coefficient). We employ various influence mechanisms that govern the influence spreading in such large-scale OSNs and use the local mean field (LMF) technique to analyze these online social networks wherein states of nodes can be changed by various influence mechanisms. We extend our model for advertising with multiple rating levels. Extensive simulations are carried out to validate our models, which can provide insight on designing efficient advertising strategies in online social networks.

Scalable influence maximization for independent cascade model in large-scale social networks

Influence maximization, defined by Kempe et al. (SIGKDD 2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large-scale online social networks. Prior solutions, such as the greedy algorithm of Kempe et al. (SIGKDD 2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this article, we design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments. Our algorithm has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm. Our results from extensive simulations on several real-world and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem: (a) our algorithm scales beyond million-sized graphs where the greedy algorithm becomes infeasible, and (b) in all size ranges, our algorithm performs consistently well in influence spread—it is always among the best algorithms, and in most cases it significantly outperforms all other scalable heuristics to as much as 100–260% increase in influence spread.

Weak ties: Subtle role of information diffusion in online social networks

As a social media, online social networks play a vital role in the social information diffusion. However, due to its unique complexity, the mechanism of the diffusion in online social networks is different from the ones in other types of networks and remains unclear to us. Meanwhile, few works have been done to reveal the coupled dynamics of both the structure and the diffusion of online social networks. To this end, in this paper, we propose a model to investigate how the structure is coupled with the diffusion in online social networks from the view of weak ties. Through numerical experiments on large-scale online social networks, we find that in contrast to some previous research results, selecting weak ties preferentially to republish cannot make the information diffuse quickly, while random selection can achieve this goal. However, when we remove the weak ties gradually, the coverage of the information will drop sharply even in the case of random selection. We also give a reasonable explanation for this by extra analysis and experiments. Finally, we conclude that weak ties play a subtle role in the information diffusion in online social networks. On one hand, they act as bridges to connect isolated local communities together and break through the local trapping of the information. On the other hand, selecting them as preferential paths to republish cannot help the information spread further in the network. As a result, weak ties might be of use in the control of the virus spread and the private information diffusion in real-world applications.

Towards real-time community detection in large networks

The recent boom of large-scale online social networks (OSNs) both enables and necessitates the use of parallelizable and scalable computational techniques for their analysis. We examine the problem of real-time community detection and a recently proposed linear time- O(m) on a network with m edges-label propagation, or "epidemic" community detection algorithm. We identify characteristics and drawbacks of the algorithm and extend it by incorporating different heuristics to facilitate reliable and multifunctional real-time community detection. With limited computational resources, we employ the algorithm on OSN data with 1 x 10(6) nodes and about 58 x 10(6) directed edges. Experiments and benchmarks reveal that the extended algorithm is not only faster but its community detection accuracy compares favorably over popular modularity-gain optimization algorithms known to suffer from their resolution limits.