Pub Date : 2019-11-27DOI: 10.1186/s40649-019-0072-3
Christopher Diaz, A. Nikolaev, Abhinav Perla, Alexander Veremyev, E. Pasiliao
{"title":"Robust communication network formation: a decentralized approach","authors":"Christopher Diaz, A. Nikolaev, Abhinav Perla, Alexander Veremyev, E. Pasiliao","doi":"10.1186/s40649-019-0072-3","DOIUrl":"https://doi.org/10.1186/s40649-019-0072-3","url":null,"abstract":"","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65734331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-25DOI: 10.1186/s40649-021-00091-2
Andrew C. Phillips, M. Irfan, Luca Ostertag-Hill
{"title":"Spheres of legislation: polarization and most influential nodes in behavioral context","authors":"Andrew C. Phillips, M. Irfan, Luca Ostertag-Hill","doi":"10.1186/s40649-021-00091-2","DOIUrl":"https://doi.org/10.1186/s40649-021-00091-2","url":null,"abstract":"","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-021-00091-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42307328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-19DOI: 10.1186/s40649-019-0073-2
Vincenza Carchiolo, Marco Grassia, Alessandro Longheu, Michele Malgeri, Giuseppe Mangioni
Many systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.
{"title":"Network robustness improvement via long-range links","authors":"Vincenza Carchiolo, Marco Grassia, Alessandro Longheu, Michele Malgeri, Giuseppe Mangioni","doi":"10.1186/s40649-019-0073-2","DOIUrl":"https://doi.org/10.1186/s40649-019-0073-2","url":null,"abstract":"Many systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"46 6","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2019-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-06DOI: 10.1186/s40649-019-0071-4
Han Hu, NhatHai Phan, Soon A. Chun, James Geller, Huy Vo, Xinyue Ye, Ruoming Jin, Kele Ding, Deric Kenne, Dejing Dou
Drug abuse continues to accelerate towards becoming the most severe public health problem in the United States. The ability to detect drug-abuse risk behavior at a population scale, such as among the population of Twitter users, can help us to monitor the trend of drug-abuse incidents. Unfortunately, traditional methods do not effectively detect drug-abuse risk behavior, given tweets. This is because: (1) tweets usually are noisy and sparse and (2) the availability of labeled data is limited. To address these challenging problems, we propose a deep self-taught learning system to detect and monitor drug-abuse risk behaviors in the Twitter sphere, by leveraging a large amount of unlabeled data. Our models automatically augment annotated data: (i) to improve the classification performance and (ii) to capture the evolving picture of drug abuse on online social media. Our extensive experiments have been conducted on three million drug-abuse-related tweets with geo-location information. Results show that our approach is highly effective in detecting drug-abuse risk behaviors.
{"title":"An insight analysis and detection of drug-abuse risk behavior on Twitter with self-taught deep learning","authors":"Han Hu, NhatHai Phan, Soon A. Chun, James Geller, Huy Vo, Xinyue Ye, Ruoming Jin, Kele Ding, Deric Kenne, Dejing Dou","doi":"10.1186/s40649-019-0071-4","DOIUrl":"https://doi.org/10.1186/s40649-019-0071-4","url":null,"abstract":"Drug abuse continues to accelerate towards becoming the most severe public health problem in the United States. The ability to detect drug-abuse risk behavior at a population scale, such as among the population of Twitter users, can help us to monitor the trend of drug-abuse incidents. Unfortunately, traditional methods do not effectively detect drug-abuse risk behavior, given tweets. This is because: (1) tweets usually are noisy and sparse and (2) the availability of labeled data is limited. To address these challenging problems, we propose a deep self-taught learning system to detect and monitor drug-abuse risk behaviors in the Twitter sphere, by leveraging a large amount of unlabeled data. Our models automatically augment annotated data: (i) to improve the classification performance and (ii) to capture the evolving picture of drug abuse on online social media. Our extensive experiments have been conducted on three million drug-abuse-related tweets with geo-location information. Results show that our approach is highly effective in detecting drug-abuse risk behaviors.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"46 12","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2019-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1186/s40649-019-0070-5
M. Kapeller, Georg Jäger, M. Füllsack
{"title":"Homophily in networked agent-based models: a method to generate homophilic attribute distributions to improve upon random distribution approaches","authors":"M. Kapeller, Georg Jäger, M. Füllsack","doi":"10.1186/s40649-019-0070-5","DOIUrl":"https://doi.org/10.1186/s40649-019-0070-5","url":null,"abstract":"","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-019-0070-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65734753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-14DOI: 10.1186/s40649-019-0068-z
Hanjo D. Boekhout, Walter A. Kosters, Frank W. Takes
This paper proposes novel algorithms for efficiently counting complex network motifs in dynamic networks that are changing over time. Network motifs are small characteristic configurations of a few nodes and edges, and have repeatedly been shown to provide insightful information for understanding the meso-level structure of a network. Here, we deal with counting more complex temporal motifs in large-scale networks that may consist of millions of nodes and edges. The first contribution is an efficient approach to count temporal motifs in multilayer networks and networks with partial timing, two prevalent aspects of many real-world complex networks. We analyze the complexity of these algorithms and empirically validate their performance on a number of real-world user communication networks extracted from online knowledge exchange platforms. Among other things, we find that the multilayer aspects provide significant insights in how complex user interaction patterns differ substantially between online platforms. The second contribution is an analysis of the viability of motif counting algorithms for motifs that are larger than the triad motifs studied in previous work. We provide a novel categorization of motifs of size four, and determine how and at what computational cost these motifs can still be counted efficiently. In doing so, we delineate the “computational frontier” of temporal motif counting algorithms.
{"title":"Efficiently counting complex multilayer temporal motifs in large-scale networks","authors":"Hanjo D. Boekhout, Walter A. Kosters, Frank W. Takes","doi":"10.1186/s40649-019-0068-z","DOIUrl":"https://doi.org/10.1186/s40649-019-0068-z","url":null,"abstract":"This paper proposes novel algorithms for efficiently counting complex network motifs in dynamic networks that are changing over time. Network motifs are small characteristic configurations of a few nodes and edges, and have repeatedly been shown to provide insightful information for understanding the meso-level structure of a network. Here, we deal with counting more complex temporal motifs in large-scale networks that may consist of millions of nodes and edges. The first contribution is an efficient approach to count temporal motifs in multilayer networks and networks with partial timing, two prevalent aspects of many real-world complex networks. We analyze the complexity of these algorithms and empirically validate their performance on a number of real-world user communication networks extracted from online knowledge exchange platforms. Among other things, we find that the multilayer aspects provide significant insights in how complex user interaction patterns differ substantially between online platforms. The second contribution is an analysis of the viability of motif counting algorithms for motifs that are larger than the triad motifs studied in previous work. We provide a novel categorization of motifs of size four, and determine how and at what computational cost these motifs can still be counted efficiently. In doing so, we delineate the “computational frontier” of temporal motif counting algorithms.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"46 8","pages":"1-34"},"PeriodicalIF":0.0,"publicationDate":"2019-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-08DOI: 10.1186/s40649-019-0065-2
Yanxia Pang, Na Wang, Y. Zhang, Yuanyuan Jin, Wendi Ji, Wenan Tan
{"title":"Prerequisite-related MOOC recommendation on learning path locating","authors":"Yanxia Pang, Na Wang, Y. Zhang, Yuanyuan Jin, Wendi Ji, Wenan Tan","doi":"10.1186/s40649-019-0065-2","DOIUrl":"https://doi.org/10.1186/s40649-019-0065-2","url":null,"abstract":"","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-019-0065-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65734724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-24DOI: 10.1186/s40649-019-0067-0
Tong Yan, Yunpeng Gao, Nan Zhang
In this paper, we address privacy issues related to ranked retrieval model in web databases, each of which takes private attributes as part of input in the ranking function. Many web databases keep private attributes invisible to public and believe that the adversary is unable to reveal the private attribute values from query results. However, prior research (Rahman et al. in Proc VLDB Endow 8:1106–17, 2015) studied the problem of rank-based inference of private attributes over web databases. They found that one can infer the value of private attributes of a victim tuple by issuing well-designed queries through a top-k query interface. To address the privacy issue, in this paper, we propose a novel privacy-preserving framework. Our framework protects private attributes’ privacy not only under inference attacks but also under arbitrary attack methods. In particular, we classify adversaries into two widely existing categories: domain-ignorant and domain-expert adversaries. Then, we develop equivalent set with virtual tuples (ESVT) for domain-ignorant adversaries and equivalent set with true tuples (ESTT) for domain-expert adversaries. The ESVT and the ESTT are the primary parts of our privacy-preserving framework. To evaluate the performance, we define a measurement of privacy guarantee for private attributes and measurements for utility loss. We prove that both ESVT and ESTT achieve the privacy guarantee. We also develop heuristic algorithms for ESVT and ESTT, respectively, under the consideration of minimizing utility loss. We demonstrate the effectiveness of our techniques through theoretical analysis and extensive experiments over real-world dataset.
在本文中，我们解决了与web数据库中排名检索模型相关的隐私问题，每个数据库都将私有属性作为排名函数输入的一部分。许多web数据库保持私有属性对公共不可见，并认为攻击者无法从查询结果中揭示私有属性值。然而，先前的研究(Rahman et al. in Proc VLDB Endow 8:1106 - 17,2015)研究了基于web数据库的私有属性的排名推理问题。他们发现，可以通过top-k查询接口发出设计良好的查询来推断受害元组的私有属性值。为了解决隐私问题，本文提出了一种新的隐私保护框架。该框架既能在推理攻击下保护私有属性的隐私，又能在任意攻击方式下保护私有属性的隐私。特别地，我们将对手分为两类:领域无知和领域专家对手。然后，我们针对领域无知的对手开发了具有虚拟元组的等价集(ESVT)，针对领域专家的对手开发了具有真元组的等价集(ESTT)。ESVT和ESTT是我们隐私保护框架的主要部分。为了评估性能，我们定义了私有属性的隐私保证度量和效用损失度量。我们证明了ESVT和ESTT都实现了隐私保证。在最小化效用损失的考虑下，我们分别开发了ESVT和ESTT的启发式算法。我们通过理论分析和对现实世界数据集的广泛实验证明了我们技术的有效性。
{"title":"A privacy-preserving framework for ranked retrieval model","authors":"Tong Yan, Yunpeng Gao, Nan Zhang","doi":"10.1186/s40649-019-0067-0","DOIUrl":"https://doi.org/10.1186/s40649-019-0067-0","url":null,"abstract":"In this paper, we address privacy issues related to ranked retrieval model in web databases, each of which takes private attributes as part of input in the ranking function. Many web databases keep private attributes invisible to public and believe that the adversary is unable to reveal the private attribute values from query results. However, prior research (Rahman et al. in Proc VLDB Endow 8:1106–17, 2015) studied the problem of rank-based inference of private attributes over web databases. They found that one can infer the value of private attributes of a victim tuple by issuing well-designed queries through a top-k query interface. To address the privacy issue, in this paper, we propose a novel privacy-preserving framework. Our framework protects private attributes’ privacy not only under inference attacks but also under arbitrary attack methods. In particular, we classify adversaries into two widely existing categories: domain-ignorant and domain-expert adversaries. Then, we develop equivalent set with virtual tuples (ESVT) for domain-ignorant adversaries and equivalent set with true tuples (ESTT) for domain-expert adversaries. The ESVT and the ESTT are the primary parts of our privacy-preserving framework. To evaluate the performance, we define a measurement of privacy guarantee for private attributes and measurements for utility loss. We prove that both ESVT and ESTT achieve the privacy guarantee. We also develop heuristic algorithms for ESVT and ESTT, respectively, under the consideration of minimizing utility loss. We demonstrate the effectiveness of our techniques through theoretical analysis and extensive experiments over real-world dataset.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"46 11","pages":"1-25"},"PeriodicalIF":0.0,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we develop a more general framework of block-structured Markov processes in the queueing study of blockchain systems, which can provide analysis both for the stationary performance measures and for the sojourn time of any transaction or block. In addition, an original aim of this paper is to generalize the two-stage batch-service queueing model studied in Li et al. (Blockchain queue theory. In: International conference on computational social networks. Springer: New York; 2018. p. 25–40) both “from exponential to phase-type” service times and “from Poisson to MAP” transaction arrivals. Note that the MAP transaction arrivals and the two stages of PH service times make our blockchain queue more suitable to various practical conditions of blockchain systems with crucial factors, for example, the mining processes, the block generations, the blockchain building and so forth. For such a more general blockchain queueing model, we focus on two basic research aspects: (1) using the matrix-geometric solution, we first obtain a sufficient stable condition of the blockchain system. Then, we provide simple expressions for the average stationary number of transactions in the queueing waiting room and the average stationary number of transactions in the block. (2) However, on comparing with Li et al. (2018), analysis of the transaction–confirmation time becomes very difficult and challenging due to the complicated blockchain structure. To overcome the difficulties, we develop a computational technique of the first passage times by means of both the PH distributions of infinite sizes and the RG factorizations. Finally, we hope that the methodology and results given in this paper will open a new avenue to queueing analysis of more general blockchain systems in practice and can motivate a series of promising future research on development of blockchain technologies.
在本文中，我们在区块链系统的排队研究中开发了一个更通用的块结构马尔可夫过程框架，该框架可以为平稳性能度量和任何交易或块的停留时间提供分析。此外，本文的一个原始目的是推广Li等人在区块链队列理论中研究的两阶段批处理服务排队模型。参考文献:计算社会网络国际会议。斯普林格出版社:纽约;2018. 第25-40页)服务时间“从指数型到阶段型”和事务到达“从泊松到MAP”。请注意，MAP事务到达和PH服务时间的两个阶段使我们的区块链队列更适合区块链系统的各种实际条件，具有关键因素，例如挖矿过程，区块生成，区块链构建等。对于这样一种更通用的区块链排队模型，我们重点研究了两个基本方面:(1)利用矩阵-几何解，首先获得了区块链系统的充分稳定条件。然后，我们给出了排队等候室平均固定交易数和区块平均固定交易数的简单表达式。(2)然而，与Li et al.(2018)相比，由于区块链结构复杂，交易确认时间的分析变得非常困难和具有挑战性。为了克服这一困难，我们利用无限大小的PH分布和RG分解，开发了一种计算首次通过时间的技术。最后，我们希望本文给出的方法和结果将为实践中更通用的区块链系统的排队分析开辟一条新的途径，并可以激发一系列有前途的区块链技术发展研究。
{"title":"Markov processes in blockchain systems","authors":"Quan-Lin Li, Jing-Yu Ma, Yan-Xia Chang, Fan-Qi Ma, Hai-Bo Yu","doi":"10.1186/s40649-019-0066-1","DOIUrl":"https://doi.org/10.1186/s40649-019-0066-1","url":null,"abstract":"In this paper, we develop a more general framework of block-structured Markov processes in the queueing study of blockchain systems, which can provide analysis both for the stationary performance measures and for the sojourn time of any transaction or block. In addition, an original aim of this paper is to generalize the two-stage batch-service queueing model studied in Li et al. (Blockchain queue theory. In: International conference on computational social networks. Springer: New York; 2018. p. 25–40) both “from exponential to phase-type” service times and “from Poisson to MAP” transaction arrivals. Note that the MAP transaction arrivals and the two stages of PH service times make our blockchain queue more suitable to various practical conditions of blockchain systems with crucial factors, for example, the mining processes, the block generations, the blockchain building and so forth. For such a more general blockchain queueing model, we focus on two basic research aspects: (1) using the matrix-geometric solution, we first obtain a sufficient stable condition of the blockchain system. Then, we provide simple expressions for the average stationary number of transactions in the queueing waiting room and the average stationary number of transactions in the block. (2) However, on comparing with Li et al. (2018), analysis of the transaction–confirmation time becomes very difficult and challenging due to the complicated blockchain structure. To overcome the difficulties, we develop a computational technique of the first passage times by means of both the PH distributions of infinite sizes and the RG factorizations. Finally, we hope that the methodology and results given in this paper will open a new avenue to queueing analysis of more general blockchain systems in practice and can motivate a series of promising future research on development of blockchain technologies.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"45 2","pages":"1-28"},"PeriodicalIF":0.0,"publicationDate":"2019-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-27DOI: 10.1186/s40649-020-00083-8
Hông-Lan Botterman, Robin Lamarche-Perrin
{"title":"Link weights recovery in heterogeneous information networks","authors":"Hông-Lan Botterman, Robin Lamarche-Perrin","doi":"10.1186/s40649-020-00083-8","DOIUrl":"https://doi.org/10.1186/s40649-020-00083-8","url":null,"abstract":"","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-020-00083-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44061832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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