2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献

Improved Triangle Counting in Graph Streams: Power of Multi-Sampling 图流中改进的三角形计数:多采样的能力

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508789

Neeraj Kavassery-Parakkat, K. Hanjani, A. Pavan

{"title":"Improved Triangle Counting in Graph Streams: Power of Multi-Sampling","authors":"Neeraj Kavassery-Parakkat, K. Hanjani, A. Pavan","doi":"10.1109/ASONAM.2018.8508789","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508789","url":null,"abstract":"Some of the well known streaming algorithms to estimate number of triangles in a graph stream work as follows: Sample a single triangle with high enough probability and repeat this basic step to obtain a global triangle count. For example, the algorithm due to Buriol et al. (PODS 2006) uniformly at random picks a single vertex v and a single edge e and checks whether the two cross edges that connect $v$ to $e$ appear in the stream. Similarly, the neighborhood sampling algorithm (PVLDB 2013) attempts to sample a triangle by randomly choosing a single vertex v, a single neighbor $u$ of $v$ and waits for a third edge that completes the triangle. In both the algorithms, the basic sampling step is repeated multiple times to obtain an estimate for the global triangle count in the input graph stream. In this work, we propose a multi-sampling variant of these algorithms: In case of Buriol et al's algorithm, instead of randomly choosing a single vertex and edge, randomly sample multiple vertices and multiple edges and collect cross edges that connect sampled vertices to the sampled edges. In case of neighborhood sampling algorithm, randomly pick multiple edges and pick multiple neighbors of them. We provide a theoretical analysis of these algorithms and prove that these new algorithms improve upon the known space and accuracy bounds. We experimentally show that these algorithms outperform well known triangle counting streaming algorithms.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127185241","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: 4

One Size Does Not Fit All: Predicting Product Returns in E-Commerce Platforms 一种模式不适合所有:预测电子商务平台的产品退货

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508486

Tanuj Joshi, Animesh Mukherjee, Girish Ippadi

{"title":"One Size Does Not Fit All: Predicting Product Returns in E-Commerce Platforms","authors":"Tanuj Joshi, Animesh Mukherjee, Girish Ippadi","doi":"10.1109/ASONAM.2018.8508486","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508486","url":null,"abstract":"Providing easy and hassle-free product returns have become a norm for e-commerce companies. However, this flexibility on the part of the customer causes the respective e-commerce companies to incur heavy losses because of the delivery logistics involved and the eventual lower resale value of the product returned. In this paper, we consider data from one of the leading Indian e-commerce companies and investigate the problem of product returns across different lifestyle verticals. One of the striking observations from our measurements is that most of the returns take place for apparels/garments and the major reason for the return as cited by the customers is the “size/fit” issue. Here we develop, based on past purchase/return data, a model that given a user, a brand and a size of the product can predict whether the user is going to eventually return the product. The methodological novelty of our model is that it combines concepts from network science and machine learning to make the predictions. Across three different major verticals of various sizes, we obtain overall F-score improvements between 10%–25% over a naïve baseline where the clusters are obtained using simple random walk with restarts.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127195845","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

Text Mining for Plagiarism Detection: Multivariate Pattern Detection for Recognition of Text Similarities 用于抄袭检测的文本挖掘:用于文本相似度识别的多元模式检测

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508265

Konstantinos F. Xylogiannopoulos, P. Karampelas, R. Alhajj

引用次数: 6

ASONAM 2018 Program Committee ASONAM 2018项目委员会

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/asonam.2018.8508303

A. Caimo, A. Bozzon, Alessandro Epasto, Aneesh Sharma, Anirban Dasgupta, Cristina Ioana Muntean, Edgar Meij, Edoardo Serra, F. M. Nardini, Guolei Yang, H. Rabiee, Hongyun Cai, Hongzhi Yin, Huan Liu, H. Rangwala, G. Mason, Ingmar Weber, Ingo Scholtes, Jie Tang

引用次数: 0

Implicit Entity Linking Through Ad-Hoc Retrieval 通过自组织检索的隐式实体链接

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508612

Hawre Hosseini, Tam T. Nguyen, E. Bagheri

引用次数: 7

Weakly Supervised Learning for Fake News Detection on Twitter 微博假新闻检测的弱监督学习

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508520

Stefan Helmstetter, Heiko Paulheim

{"title":"Weakly Supervised Learning for Fake News Detection on Twitter","authors":"Stefan Helmstetter, Heiko Paulheim","doi":"10.1109/ASONAM.2018.8508520","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508520","url":null,"abstract":"The problem of automatic detection of fake news in social media, e.g., on Twitter, has recently drawn some attention. Although, from a technical perspective, it can be regarded as a straight-forward, binary classification problem, the major challenge is the collection of large enough training corpora, since manual annotation of tweets as fake or non-fake news is an expensive and tedious endeavor. In this paper, we discuss a weakly supervised approach, which automatically collects a large-scale, but very noisy training dataset comprising hundreds of thousands of tweets. During collection, we automatically label tweets by their source, i.e., trustworthy or untrustworthy source, and train a classifier on this dataset. We then use that classifier for a different classification target, i.e., the classification of fake and non-fake tweets. Although the labels are not accurate according to the new classification target (not all tweets by an untrustworthy source need to be fake news, and vice versa), we show that despite this unclean inaccurate dataset, it is possible to detect fake news with an F1 score of up to 0.9.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130555804","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: 134

Model Bots, not Humans on Social Media 在社交媒体上模仿机器人，而不是真人

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508279

Nikan Chavoshi, A. Mueen

引用次数: 11

Positive Cognitive Restructuring Through an App Based on Context Messages 基于上下文信息的应用程序的积极认知重构

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508849

J. Conesa, D. Gañán, A. Pérez-Navarro, R. Nieto, Gemma Ruiz, F. S. Rubió, B. Sora

引用次数: 1

A Parallel Community Detection Algorithm Based on Incremental Clustering in Dynamic Network 动态网络中基于增量聚类的并行社区检测算法

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508730

Cuiyun Zhang, Yunlei Zhang, Bin Wu

{"title":"A Parallel Community Detection Algorithm Based on Incremental Clustering in Dynamic Network","authors":"Cuiyun Zhang, Yunlei Zhang, Bin Wu","doi":"10.1109/ASONAM.2018.8508730","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508730","url":null,"abstract":"Dynamic community detection is a key method for the research of network evolution. However, most existing dynamic community detection algorithms are time-consuming in dealing with large-scale networks. Moreover, most current parallel community detection algorithms are static and they ignore the changes of network structure over time. In this paper, we propose a novel parallel algorithm based on incremental vertices, which is able to process large-scale dynamic networks, called PICD. In PICD algorithm, the revised Parallel Weighted Community Clustering (PWCC) metric is conductive to a convenient calculation, which is more sensitive to community structure compared to other metrics. The PICD approach consists of two main steps. Firstly, it identifies the incremental vertices in the dynamic network. Secondly, it maximizes the PWCC of the entire network by merely adjusting the community membership of incremental vertices to capture community structure in high quality. The results of experiments on both the synthetic and real world networks demonstrate that the PICD algorithm achieves a higher accuracy and efficiency. Moreover, it performs more stable than most of the baseline methods. The experiments also show that PICD algorithm takes an almost linear time with the growth of the network scale.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116335284","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: 3

Characterizing Politically Engaged Users' Behavior During the 2016 US Presidential Campaign 2016年美国总统竞选期间参与政治的用户行为特征

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508459

J. Caetano, J. Almeida, H. T. Marques-Neto

引用次数: 8