Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017最新文献_第3页

Principal Patern Mining on Graphs 图上的主模式挖掘

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3116202

C. Kuo, Mi-Yen Yeh, J. Pei

引用次数: 0

Attributed Graph Clustering: an Attribute-aware Graph Embedding Approach 属性图聚类:一种属性感知的图嵌入方法

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110092

Esra Akbas, Peixiang Zhao

{"title":"Attributed Graph Clustering: an Attribute-aware Graph Embedding Approach","authors":"Esra Akbas, Peixiang Zhao","doi":"10.1145/3110025.3110092","DOIUrl":"https://doi.org/10.1145/3110025.3110092","url":null,"abstract":"Graph clustering is a fundamental problem in social network analysis, the goal of which is to group vertices of a graph into a series of densely knitted clusters with each cluster well separated from all the others. Classical graph clustering methods take advantage of the graph topology to model and quantify vertex proximity. With the proliferation of rich graph contents, such as user profiles in social networks, and gene annotations in protein interaction networks, it is essential to consider both the structure and content information of graphs for high-quality graph clustering. In this paper, we propose a graph embedding approach to clustering content-enriched graphs. The key idea is to embed each vertex of a graph into a continuous vector space where the localized structural and attributive information of vertices can be encoded in a unified, latent representation. Specifically, we quantify vertex-wise attribute proximity into edge weights, and employ truncated, attribute-aware random walks to learn the latent representations for vertices. We evaluate our attribute-aware graph embedding method in real-world attributed graphs, and the results demonstrate its effectiveness in comparison with state-of-the-art algorithms.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115834827","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}

引用次数: 23

An Analysis of Citation Recommender Systems: Beyond the Obvious 引文推荐系统分析:超越表象

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110150

Haofeng Jia, Erik Saule

{"title":"An Analysis of Citation Recommender Systems: Beyond the Obvious","authors":"Haofeng Jia, Erik Saule","doi":"10.1145/3110025.3110150","DOIUrl":"https://doi.org/10.1145/3110025.3110150","url":null,"abstract":"As science advances, the academic community has published millions of research papers. Researchers devote time and effort to search relevant manuscripts when writing a paper or simply to keep up with current research. In this paper, we consider the problem of citation recommendation by extending a set of known-to-be-relevant references. Our analysis shows the degrees of cited papers in the subgraph induced by the citations of a paper, called projection graph, follow a power law distribution. Existing popular methods are only good at finding the long tail papers, the ones that are highly connected to others. In other words, the majority of cited papers are loosely connected in the projection graph but they are not going to be found by existing methods. To address this problem, we propose to combine author, venue and keyword information to interpret the citation behavior behind those loosely connected papers. Results show that different methods are finding cited papers with widely different properties. We suggest multiple recommended lists by different algorithms could satisfy various users for a real citation recommendation system.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114405281","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}

引用次数: 20

A Computational Framework for Influence Networks: Application to Clergy Influence in HIV/AIDS Outreach 影响网络的计算框架:应用于神职人员对艾滋病毒/艾滋病的影响

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3125430

Eva K. Lee, Zixing Wang

{"title":"A Computational Framework for Influence Networks: Application to Clergy Influence in HIV/AIDS Outreach","authors":"Eva K. Lee, Zixing Wang","doi":"10.1145/3110025.3125430","DOIUrl":"https://doi.org/10.1145/3110025.3125430","url":null,"abstract":"Strong social networks can encourage healthy behaviors. In this paper, we introduce a sociology-based computational framework for influence networks. The model construct is generic and is applicable to diverse social network analysis. We demonstrate its usage in calibrating the positive influence of church clergy in spreading HIV/AIDs information in a large metropolitan city. Five experiments are designed to contrast influence with respect to the interaction style between clergy and churchgoers. Competitive and non-competitive knowledge dissemination are also analyzed. The results show that when only one set of information exists, the spreading scope is directly proportional to the product of population size and the disease infection rate. When competing information is present, the importance of clergy in spreading the information decreases when the original propagation sources are ample. However, if sufficient interaction and trust are present among the clergy and the participants, the clergy's positive influence remains significant despite pre-existing knowledge. The generalized framework requires minimal regional data to establish the influence network. It provides useful policy insights for decision makers to determine effective avenues for information dissemination through community influencers.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126627772","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}

引用次数: 0

Analyzing the Use of Twitter to Disseminate Visual Impairments Awareness Information 利用Twitter传播视觉障碍意识信息的分析

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110137

Majed Al Zayer, M. H. Gunes

{"title":"Analyzing the Use of Twitter to Disseminate Visual Impairments Awareness Information","authors":"Majed Al Zayer, M. H. Gunes","doi":"10.1145/3110025.3110137","DOIUrl":"https://doi.org/10.1145/3110025.3110137","url":null,"abstract":"People with visual impairments have been surrounded with myths and misconceptions that have made it challenging for them to live as productive members of the society and have partly contributed in making the majority live in substandard living conditions. To shift the public's focus from being on the disability to be on the abilities of the visually impaired, non-profit organizations and government agencies have conducted a series of campaigns to spread awareness about visual impairments. Social media platforms, such as Twitter, has become the primary channel to diffuse information to the public. The goal of this paper is to analyze the use of Twitter as a social media platform to spread information during visual impairments awareness campaigns. We focused our analysis on five key concerns: (i) the characteristics of active users during the event, (ii) the major players in information dissemination, (iii) the tweets' common topics (iv) the reachability of information, and (v) the temporal tweeting behavior. We report the results of the campaigns along with the design of an effective communication strategy of a campaign.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123198588","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}

引用次数: 5

Choose The Best!: Ranking Group of Users In Collaborative Networks 选择最好的!:协作网络中的用户排序组

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3120991

Nunzio Cassavia, S. Flesca, E. Masciari

引用次数: 0

Cryptographic Sequence on Variant Maps 变异映射上的密码序列

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110152

Zhonghao Yang

引用次数: 0

A Unified Framework to Estimate Global and Local Graphlet Counts for Streaming Graphs 估计流图全局和局部Graphlet计数的统一框架

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110042

Xiaowei Chen, John C.S. Lui

{"title":"A Unified Framework to Estimate Global and Local Graphlet Counts for Streaming Graphs","authors":"Xiaowei Chen, John C.S. Lui","doi":"10.1145/3110025.3110042","DOIUrl":"https://doi.org/10.1145/3110025.3110042","url":null,"abstract":"Counting small connected subgraph patterns called graphlets is emerging as a powerful tool for exploring topological structure of networks and for analysis of roles of individual nodes. Graphlets have numerous applications ranging from biology to network science. Computing graphlet counts for \"dynamic graphs\" is highly challenging due to the streaming nature of the input, sheer size of the graphs, and superlinear time complexity of the problem. Few practical results are known under the massive streaming graphs setting. In this work, we propose a \"unified framework\" to estimate the graphlet counts of the whole graph as well as the graphlet counts of individual nodes under the streaming graph setting. Our framework subsumes previous methods and provides more flexible and accurate estimation of the graphlet counts. We propose a general unbiased estimator which can be applied to any k-node graphlets. Furthermore, efficient implementation is provided for the 3, 4-node graphlets. We perform detailed empirical study on real-world graphs, and show that our framework produces estimation of graphlet count for streaming graphs with 1.7 to 170.8 times smaller error compared with other state-of-the-art methods. Our framework also achieves high accuracy on the estimation of graphlets for each individual node which previous works could not achieve.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133526046","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}

引用次数: 9

Using supervised machine learning algorithms to detect suspicious URLs in online social networks 使用监督机器学习算法检测在线社交网络中的可疑url

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3116201

Mohammed Al-Janabi, E. Quincey, Péter András

{"title":"Using supervised machine learning algorithms to detect suspicious URLs in online social networks","authors":"Mohammed Al-Janabi, E. Quincey, Péter András","doi":"10.1145/3110025.3116201","DOIUrl":"https://doi.org/10.1145/3110025.3116201","url":null,"abstract":"The increasing volume of malicious content in social networks requires automated methods to detect and eliminate such content. This paper describes a supervised machine learning classification model that has been built to detect the distribution of malicious content in online social networks (ONSs). Multisource features have been used to detect social network posts that contain malicious Uniform Resource Locators (URLs). These URLs could direct users to websites that contain malicious content, drive-by download attacks, phishing, spam, and scams. For the data collection stage, the Twitter streaming application programming interface (API) was used and VirusTotal was used for labelling the dataset. A random forest classification model was used with a combination of features derived from a range of sources. The random forest model without any tuning and feature selection produced a recall value of 0.89. After further investigation and applying parameter tuning and feature selection methods, however, we were able to improve the classifier performance to 0.92 in recall.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121845510","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}

引用次数: 35

Streaming Graph Sampling with Size Restrictions 具有大小限制的流图采样

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110058

A. Zakrzewska, David A. Bader

{"title":"Streaming Graph Sampling with Size Restrictions","authors":"A. Zakrzewska, David A. Bader","doi":"10.1145/3110025.3110058","DOIUrl":"https://doi.org/10.1145/3110025.3110058","url":null,"abstract":"Many graph datasets originating from online social network, financial or biological sources are too large to store or analyze. The analysis of such networks may be made more tractable if they are reduced to smaller subgraphs via sampling. While most of the known graph sampling methods are designed with static graphs in mind, many real datasets are massive and rapidly growing, making streaming methods necessary. We present two new techniques, Randomly Induced Edge Sampling (RIES) and Weighted Edge Sampling (WES). Both methods sample a stream of edges in a single pass, without the need to know future properties of the stream. In contrast to previous work that focused on limiting only the number of vertices, our methods restrict the number of edges, thus truly limiting the size of the sampled subgraph. We compare the performance of RIES and WES against the previously known streaming Random Edge (RE) method on eight social network datasets. Using four structural graph properties, we find that both RIES and WES produce subgraphs that are more structurally similar to the original graph than are the subgraphs produced by streaming RE. We also examine the sensitivity of the two algorithms with respect to their parameters. The parameters of WES affect its performance in a more predictable manner and are easier to set. Both new algorithms represent an improvement in the available streaming graph analysis toolkit.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129256411","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}

引用次数: 5