Proceedings of the 2017 ACM on Conference on Information and Knowledge Management最新文献_第9页

Attentive Graph-based Recursive Neural Network for Collective Vertex Classification 基于关注图的聚点分类递归神经网络

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133081

Qiongkai Xu, Qing Wang, Chenchen Xu, Lizhen Qu

引用次数: 4

A Non-negative Symmetric Encoder-Decoder Approach for Community Detection 一种非负对称编码器-解码器社区检测方法

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132902

Bing-Jie Sun, Huawei Shen, Jinhua Gao, W. Ouyang, Xueqi Cheng

{"title":"A Non-negative Symmetric Encoder-Decoder Approach for Community Detection","authors":"Bing-Jie Sun, Huawei Shen, Jinhua Gao, W. Ouyang, Xueqi Cheng","doi":"10.1145/3132847.3132902","DOIUrl":"https://doi.org/10.1145/3132847.3132902","url":null,"abstract":"Community detection or graph clustering is crucial to understanding the structure of complex networks and extracting relevant knowledge from networked data. Latent factor model, e.g., non-negative matrix factorization and mixed membership block model, is one of the most successful methods for community detection. Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes. Existing latent factor models are mainly based on reconstructing a network from the representation of its nodes, namely network decoder, while constraining the representation to have certain desirable properties. These methods, however, lack an encoder that transforms nodes into their representation. Consequently, they fail to give a clear explanation about the meaning of a community and suffer from undesired computational problems. In this paper, we propose a non-negative symmetric encoder-decoder approach for community detection. By explicitly integrating a decoder and an encoder into a unified loss function, the proposed approach achieves better performance over state-of-the-art latent factor models for community detection task. Moreover, different from existing methods that explicitly impose the sparsity constraint on the representation of nodes, the proposed approach implicitly achieves the sparsity of node representation through its symmetric and non-negative properties, making the optimization much easier than competing methods based on sparse matrix factorization.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"82 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90817189","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: 56

SMASC 2017: First International Workshop on Social Media Analytics for Smart Cities SMASC 2017:首届智慧城市社交媒体分析国际研讨会

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133199

Manjira Sinha, Xiangnan He, A. Bozzon, Sandya Mannarswamy, P. Murukannaiah, Tridib Mukherjee

{"title":"SMASC 2017: First International Workshop on Social Media Analytics for Smart Cities","authors":"Manjira Sinha, Xiangnan He, A. Bozzon, Sandya Mannarswamy, P. Murukannaiah, Tridib Mukherjee","doi":"10.1145/3132847.3133199","DOIUrl":"https://doi.org/10.1145/3132847.3133199","url":null,"abstract":"In an increasingly digital urban setting, connected & concerned Citizens typically voice their opinions on various civic topics via social media. Efficient and scalable analysis of these citizen voices on social media to derive actionable insights is essential to the development of smart cities. The very nature of the data: heterogeneity and dynamism, the scarcity of gold standard annotated corpora, and the need for multi-dimensional analysis across space, time and semantics, makes urban social media analytics challenging. This workshop is dedicated to the theme of social media analytics for smart cities, with the aim of focusing the interest of CIKM research community on the challenges in mining social media data for urban informatics. The workshop hopes to foster collaboration between researchers working in information retrieval, social media analytics, linguistics; social scientists, and civic authorities, to develop scalable and practical systems for capturing and acting upon real world issues of cities as voiced by their citizens in social media. The aim of this workshop is to encourage researchers to develop techniques for urban analytics of social media data, with specific focus on applying these techniques to practical urban informatics applications for smart cities.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80648217","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

Estimating Event Focus Time Using Neural Word Embeddings 使用神经词嵌入估计事件焦点时间

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133131

Supratim Das, Arunav Mishra, K. Berberich, Vinay Setty

引用次数: 11

Rapid Analysis of Network Connectivity 网络连通性快速分析

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133170

Scott Freitas, Hanghang Tong, Nan Cao, Yinglong Xia

引用次数: 3

Optimizing Email Volume For Sitewide Engagement 优化电子邮件量为全站参与

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132849

Rupesh Gupta, Guanfeng Liang, Rómer Rosales

{"title":"Optimizing Email Volume For Sitewide Engagement","authors":"Rupesh Gupta, Guanfeng Liang, Rómer Rosales","doi":"10.1145/3132847.3132849","DOIUrl":"https://doi.org/10.1145/3132847.3132849","url":null,"abstract":"In this paper we focus on the problem of optimizing email volume for maximizing sitewide engagement of an online social networking service. Email volume optimization approaches published in the past have proposed optimization of email volume for maximization of engagement metrics which are impacted exclusively by email; for example, the number of sessions that begin with clicks on links within emails. The impact of email on such downstream engagement metrics can be estimated easily because of the ease of attribution of such an engagement event to an email. However, this framework is limited in its view of the ecosystem of the networking service which comprises of several tools and utilities that contribute towards delivering value to members; with email being just one such utility. Thus, in this paper we depart from previous approaches by exploring and optimizing the contribution of email to this ecosystem. In particular, we present and contrast the differential impact of email on sitewide engagement metrics for various types of users. We propose a new email volume optimization approach which maximizes sitewide engagement metrics, such as the total number of active users. This is in sharp contrast to the previous approaches whose objective has been maximization of downstream engagement metrics. We present details of our prediction function for predicting the impact of emails on a user's activeness on the mobile or web application. We describe how certain approximations to this prediction function can be made for solving the volume optimization problem, and present results from online A/B tests.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"151 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79507874","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: 18

Personalized Image Aesthetics Assessment 个性化形象美学评价

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133052

Xian-Ping Deng, C. Cui, Huidi Fang, Xiushan Nie, Yilong Yin

引用次数: 14

Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining 利用对比模式挖掘总结网络流量的重大变化

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133111

E. Chavary, S. Erfani, C. Leckie

{"title":"Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining","authors":"E. Chavary, S. Erfani, C. Leckie","doi":"10.1145/3132847.3133111","DOIUrl":"https://doi.org/10.1145/3132847.3133111","url":null,"abstract":"Extracting knowledge from the massive volumes of network traffic is an important challenge in network and security management. In particular, network managers require concise reports about significant changes in their network traffic. While most existing techniques focus on summarizing a single traffic dataset, the problem of finding significant differences between multiple datasets is an open challenge. In this paper, we focus on finding important differences between network traffic datasets, and preparing a summarized and interpretable report for security managers. We propose the use of contrast pattern mining, which finds patterns whose support differs significantly from one dataset to another. We show that contrast patterns are highly effective at extracting meaningful changes in traffic data. We also propose several evaluation metrics that reflect the interpretability of patterns for security managers. Our experimental results show that with the proposed unsupervised approach, the vast majority of extracted patterns are pure, i.e., most changes are either attack traffic or normal traffic, but not a mixture of both.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85625120","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: 7

pm-SCAN: an I/O Efficient Structural Clustering Algorithm for Large-scale Graphs pm-SCAN:大规模图形的I/O高效结构聚类算法

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133121

J. Seo, Myoung-Ho Kim

引用次数: 11

GPU-Accelerated Graph Clustering via Parallel Label Propagation 基于并行标签传播的gpu加速图聚类

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132960

Yusuke Kozawa, T. Amagasa, H. Kitagawa

{"title":"GPU-Accelerated Graph Clustering via Parallel Label Propagation","authors":"Yusuke Kozawa, T. Amagasa, H. Kitagawa","doi":"10.1145/3132847.3132960","DOIUrl":"https://doi.org/10.1145/3132847.3132960","url":null,"abstract":"Graph clustering has recently attracted much attention as a technique to extract community structures from various kinds of graph data. Since available graph data becomes increasingly large, the acceleration of graph clustering is an important issue for handling large-scale graphs. To this end, this paper proposes a fast graph clustering method using GPUs. The proposed method is based on parallelization of label propagation, one of the fastest graph clustering algorithms. Our method has the following three characteristics: (1) efficient parallelization: the algorithm of label propagation is transformed into a sequence of data-parallel primitives; (2) load balance: the method takes into account load balancing by adopting the primitives that make the load among threads and blocks well balanced; and (3) out-of-core processing: we also develop algorithms to efficiently deal with large-scale datasets that do not fit into GPU memory. Moreover, this GPU out-of-core algorithm is extended to simultaneously exploit both CPUs and GPUs for further performance gain. Extensive experiments with real-world and synthetic datasets show that our proposed method outperforms an existing parallel CPU implementation by a factor of up to 14.3 without sacrificing accuracy.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88686283","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: 17