Proceedings of the 2017 ACM on Web Science Conference最新文献_第6页

Automatic Generation of Event Timelines from Social Data 从社交数据自动生成事件时间轴

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091519

Omar Alonso, S. Tremblay, Fernando Diaz

引用次数: 5

ε - WGX: Adaptive Edge Probing for Enhancing Incomplete Networks ε - WGX:增强不完全网络的自适应边缘探测

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091492

S. Soundarajan, Tina Eliassi-Rad, Brian Gallagher, Ali Pinar

{"title":"ε - WGX: Adaptive Edge Probing for Enhancing Incomplete Networks","authors":"S. Soundarajan, Tina Eliassi-Rad, Brian Gallagher, Ali Pinar","doi":"10.1145/3091478.3091492","DOIUrl":"https://doi.org/10.1145/3091478.3091492","url":null,"abstract":"No matter how meticulously constructed, network datasets are often partially observed and incomplete. For example, most of the publicly available data from online social networking services (such as Facebook and Twitter) are collected via apps, users who make their accounts public, and/or the resources available to the researcher/practitioner. Such incompleteness can lead to inaccurate findings. We introduce the Adaptive Edge Probing problem. Suppose that one has observed a networked phenomenon via some form of sampling and has a budget to enhance the incomplete network by asking for additional information about specific nodes, with the ultimate goal of obtaining the most valuable information about the network as a whole. Which nodes should be further explored? We present ε-WGX, a network-based explore-exploit algorithm for identifying which nodes in the incomplete network to probe. Aggregated over multiple datasets and a wide range of probing budgets, we find that ε-WGX outperforms other explore-exploit strategies and baseline probing strategies. For example, for the task of adding as many nodes as possible, over incomplete networks observed via four popular sampling methods, ε-WGX outperforms the best comparison strategy by 12%-23% on average.","PeriodicalId":165747,"journal":{"name":"Proceedings of the 2017 ACM on Web Science Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126102981","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: 13

ASSIST: Automatic Summarization of Significant Structural Changes in Large Temporal Graphs ASSIST:大型时间图中重要结构变化的自动总结

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091518

C. Chelmis, R. Dani

{"title":"ASSIST: Automatic Summarization of Significant Structural Changes in Large Temporal Graphs","authors":"C. Chelmis, R. Dani","doi":"10.1145/3091478.3091518","DOIUrl":"https://doi.org/10.1145/3091478.3091518","url":null,"abstract":"Detecting outliers and anomalies in data is vital in numerous applications in areas such as security, finance, health care, and online social media. Such dynamic systems can be modeled as graphs that change over time. Even though considerable work has been performed on finding points in time at which a network notably differs from its past, little work has been done on characterizing or explaining such changes. However, in the era of big data where networked data are getting bigger and bigger, being able to summarize such changes is key for sensemaking and root cause analysis. To address this gap, we present a novel approach to summarize significant structural changes in large temporal graphs. Specifically, we propose an efficient approach to help the user understand sharp changes in the structure of the network by presenting to her only a summary of key subgraphs that contribute most to the change. Extensive evaluation on real-world datasets with ground truth demonstrates both quantitatively and qualitatively the ability of our approach to accurately detect changes and discover \"important\" structures to succinctly describe the change.","PeriodicalId":165747,"journal":{"name":"Proceedings of the 2017 ACM on Web Science Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132537227","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: 2

Deconstructing Diffusion on Tumblr: Structural and Temporal Aspects 解构Tumblr上的扩散:结构和时间方面

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091491

N. Alrajebah, L. Carr, Markus Luczak-Rösch, T. Tiropanis

{"title":"Deconstructing Diffusion on Tumblr: Structural and Temporal Aspects","authors":"N. Alrajebah, L. Carr, Markus Luczak-Rösch, T. Tiropanis","doi":"10.1145/3091478.3091491","DOIUrl":"https://doi.org/10.1145/3091478.3091491","url":null,"abstract":"Online social networks enable collectives of users to create and share content at scale. The diffusion of content through the network, and the resulting information cascades, are phenomena that have been widely investigated on various platforms, which facilitate information diffusion using diverse technical mechanisms, user interfaces and incentives. This paper focuses on Tumblr, an online microblogging social network with a core 'reblogging' functionality that allows information to diffuse across its network by appearing on multiple user blogs. The formation of any cascade network is visible as a list of reblogging events attached as notes to each appearance of the post in the cascade. In this paper, we examine cascade networks on Tumblr, recreated from the series of diffusion events, and analyse them from structural and temporal perspectives. To achieve this, we utilise a cascade construction model that create cascade networks, overcoming problems of a lack of contextual information and missing/degraded data. Finally, we compare cascades in Tumblr with those appearing on other social network platforms. Our analysis shows that popular content on Tumblr creates 'large' cascades that are deep, branching into a large number of separate and long paths, having a consistent number of reblogs at each depth and at each given time.","PeriodicalId":165747,"journal":{"name":"Proceedings of the 2017 ACM on Web Science Conference","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131910125","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

Cultural Fault Lines and Political Polarization 文化断层线和政治两极分化

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091520

Yongren Shi, Kai Mast, Ingmar Weber, Agrippa Kellum, M. Macy

引用次数: 25

Closed-Loop Opinion Formation 闭环意见形成

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091483

L. Spinelli, M. Crovella

引用次数: 9

Understanding Citizens' and Local Governments' Digital Communications During Natural Disasters: The Case of Snowstorms 理解自然灾害期间公民和地方政府的数字通信:以暴风雪为例

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091502

Lingzi Hong, Cheng Fu, P. Torrens, V. Frías-Martínez

引用次数: 28

When Friend Becomes Abuser: Evidence of Friend Abuse in Facebook 当朋友成为施虐者:Facebook中朋友虐待的证据

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3098869

Sajedul Talukder, Bogdan Carbunar

引用次数: 11

Consolidating Identities of Authors through Egonet Structure 通过自我网络结构巩固作者身份

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3098862

Janaína Gomide, Hugo Kling, D. R. Figueiredo

引用次数: 1

Predicting Research that will be Cited in Policy Documents 预测将在政策文件中引用的研究

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-13 DOI: 10.1145/3091478.3098865

B. Kale, Harish Varma Siravuri, Hamed Alhoori, M. Papka

引用次数: 10