社交网络与数据挖掘重量级讲者都到了!

——CCF ADL87《社交网络与数据挖掘》开始报名

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2017-11-14

adl logo-01学科前沿讲习班
The CCF Advanced Disciplines Lectures

CCF ADL 87

主题 社交网络与数据挖掘

  2017年12月22-24日 北京

社交网络和数据挖掘是计算机学科相关研究中的热点,其具体研究涵盖理论、关键技术以及互联网核心应用等各个方面。随着在线社交网络和物理社交网络的快速融合,社交网络正渗透到国家安全、经济发展和社会生活等各个方面,从大数据的产生、到基于群体智慧(如:众包)的数据加工、再到信息的消费,社交网络和数据挖掘的应用无处不在。社交网络分析的研究也逐渐从宏观的网络结构拓扑分析、发展到中观的社区发现等、再到更微观的社交关系、影响力以及用户行为建模等,然而社交网络数据的挖掘和分析还有很多本质上的挑战,包括用户交互、社交信息论的基础理论,社交数据挖掘的关键技术等。

本期CCF学科前沿讲习班《社交网络和数据挖掘》邀请到了社会网络分析和数据挖掘领域重量级的专家学者做主题报告。他们将对社交网络和数据挖掘的基础理论、关键技术方法以及当前热点问题进行深入浅出的介绍,并对如何开展本领域前沿技术研究等进行探讨。使参加者在了解学科热点、提高理论水平的同时,掌握最新技术趋势。

学术主任:唐杰 清华大学、刘知远 清华大学

主办单位:中国计算机学会

独家合作媒体:雷锋网

特邀讲者:

Philips Yu

Philip S. Yu is a Distinguished Professor in Computer Science at the University of Illinois at Chicago and also holds the Wexler Chair in Information Technology. Before joining UIC, Dr. Yu was with IBM, where he was manager of the Software Tools and Techniques department at the Watson Research Center. His research interest is on big data, including data mining, data stream, database and privacy. He has published more than 1,000 papers in refereed journals and conferences. He holds or has applied for more than 300 US patents. Dr. Yu is a Fellow of the ACM and the IEEE. He is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data”, and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. He also received the ICDM 2013 10-year Highest-Impact Paper Award, and the EDBT Test of Time Award (2014). He was the Editor-in-Chiefs of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).

报告题目:Broad Learning via Fusion of Social Network Information

摘要:In the era of big data, there are abundant of data available across many different data sources in various formats. “Broad Learning” is a new type of learning task, which focuses on fusing multiple large-scale information sources of diverse varieties together and carrying out synergistic data mining tasks across these fused sources in one unified analytic. Great challenges exist on “Broad Learning” for the effective fusion of relevant knowledge across different data sources, which depend upon not only the relatedness of these data sources, but also the target application problem. As social networks contain rich information, in this talk we examine how to fuse social network information to improve mining effectiveness over various applications.

Tangjie

唐杰,清华计算机系副教授、博导、CCF杰出会员、清华-工程院知识智能联合实验室主任。于2006年6月在清华大学计算机系获得博士学位,曾在康纳尔大学、香港科技大学、南安普顿大学、鲁汶大学进行学术访问。主要研究兴趣包括:社会网络分析、数据挖掘、机器学习和知识图谱,提出基于话题的社会网络影响力度量模型,利用网络影响力度量结果有效提高了用户行为预测和信息推荐精度,在多个亿级用户的社交系统得到实际验证。发表论文200余篇,包括计算机学会(CCF) A类论文70余篇,论文引用9000多次。主持研发了研究者社会网络挖掘系统AMiner,从亿级文献数据挖掘科技知识,吸引了220个国家/地区800多万独立IP访问;核心技术应用于国家科技部、自然科学基金委、中国工程院、ACM、美国艾伦人工智能研究所、搜狗、阿里巴巴、腾讯等单位。获中国人工智能学会科技进步一等奖、牛顿高级学者基金、国家自然基金委优秀青年基金、计算机学会青年科学家奖;担任国际期刊ACM TKDD的执行主编和IEEE TKDE、ACM TIST、IEEE TBD编委,担任KDD’18大会副主席、CIKM’16、WSDM’15等国际会议PC主席。

报告题目:社会影响力与行为预测

摘要:社会网络已经成为沟通真实物理世界和虚拟互联空间的桥梁。我们在互联网络中的行为直接反映了我们在真实世界的活动和情感。我将介绍在大规模真实网络中(如:微信、微博、Twitter、 AMiner等网络)如何分析用户之间的交互影响力和基于网络拓扑的结构影响力,并基于影响力预测用户行为。模型同时考虑了网络结构、用户属性和网络用户的偏好。并设计了针对大规模网络的并行学习算法。在实际真实在线社交系统中得到了验证。

ShenHuawei

沈华伟,博士,中国科学院计算技术研究所研究员,中国中文信息学会社会媒体处理专委会副主任。研究方向为网络科学和社会计算。先后获得过CCF优博、中科院优博、首届UCAS-Springer优博、中科院院长特别奖、入选首届中科院青年创新促进会、中科院计算所“学术百星”。2013年在美国东北大学进行学术访问。2015年被评为中国科学院优秀青年促进会会员(中科院优青)。获得国家科技进步二等奖、北京市科学技术二等奖、中国电子学会科学技术一等奖、中国中文信息学会钱伟长中文信息处理科学技术一等奖。出版个人专/译著3部,在网络社区发现、信息传播预测、群体行为分析、学术评价等方面取得了系列研究成果,在Science、PNAS等期刊和WWW、SIGIR、CIKM、WSDM、AAAI、IJCAI等会议上发表论文80余篇,引用2000余次。担任PNAS、IEEE TKDE、ACM TKDD等10余个学术期刊审稿人和WWW、CIKM、WSDM等20余个学术会议的程序委员会委员。

报告题目:在线社交媒体中的信息传播预测

摘要:近年来,以微博、微信等为代表的在线社会媒体逐渐成为人们发布、传播和获取信息的主要媒介。社会媒体汇聚了大量的用户关系数据和信息传播数据,为分析和研究人类社会活动提供了弥足珍贵的数据资源。社会媒体中数据多源异构、个体间关系繁杂、信息传播突发等特点给社会媒体分析提出了科学技术挑战。分析社交网络的结构规律、挖掘用户行为的固有模式、探索网络信息传播的内在机理、研究高效的社交网络分析与网络信息传播预测方法,有利于提升对在线社会媒体的科学认知水平和有效利用能力。报告将从网络结构分析、网络表达学习、网络信息传播预测等几个方面介绍报告人近几年在在线社会媒体中的信息传播预测方面的研究成果。

宋国杰

宋国杰,北京大学信息科学技术学院副教授,智能交通系统研究中心副主任。主要从事数据挖掘、机器学习、社会网络分析和智能交通系统等方面的研发工作。主持20多项国家级纵向课题和横向课题。发表包括国际顶级期刊TKDE、TPDS、TITS、Scientific Report以及国际顶级会议 KDD、AAAI、WWW 等的相关论文70余篇。研究成果获“2012 年度中国公路学会科学技术奖一等奖”、“2012 年度山西省科学技术奖二等奖”和“2013 年度中国公路学会科学技术奖一等奖”。教学成果两度获得北京大学教学成果一等奖;国家级精品课程《数据结构与算法》主讲教师。

报告题目:社会网络信息传播影响最大化挖掘

摘要:网络信息传播挖掘研究是近年来社交网络分析领域的热点问题。报告将重点介绍两方面的研究工作:传播影响最大化(Influence Maximization)和网络推断(Network Inference)。前者主要研究在既定传播模型下,如何高效寻找社交网络中信息传播影响力最大的Top-k节点集合,而后者则是在给定观测到信息传播级联数据集的基础上,推断出隐藏的、不可直接观测的社交网络拓扑结构。报告将重点介绍这两类工作的代表性研究成果,并对未来发展进行展望。

WangWei

Wei Wang is the Leonard Kleinrock Chair Professor of Computer Science at University of California, Los Angeles and the director of the Scalable Analytics Institute (ScAi). She is a co-director of the NIH BD2K Centers-Coordination Center. She received her PhD degree in Computer Science from the University of California, Los Angeles in 1999. She was a professor in Computer Science at the University of North Carolina at Chapel Hill from 2002 to 2012, and was a research staff member at the IBM T. J. Watson Research Center between 1999 and 2002. Dr. Wang's research interests include big data analytics, data mining, bioinformatics and computational biology, and databases. She has filed seven patents, and has published one monograph and more than one hundred ninety research papers in international journals and major peer-reviewed conference proceedings. Dr. Wang received the IBM Invention Achievement Awards in 2000 and 2001. She was the recipient of an NSF Faculty Early Career Development (CAREER) Award in 2005. She was named a Microsoft Research New Faculty Fellow in 2005. She was honored with the 2007 Phillip and Ruth Hettleman Prize for Artistic and Scholarly Achievement at UNC. She was recognized with an IEEE ICDM Outstanding Service Award in 2012, an Okawa Foundation Research Award in 2013, and an ACM SIGKDD Service Award in 2016. Dr. Wang has been an associate editor of the IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Big Data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, ACM Transactions on Knowledge Discovery in Data, Journal of Computational Biology, Journal of Knowledge and Information Systems, Data Mining and Knowledge Discovery, and International Journal of Knowledge Discovery in Bioinformatics. She serves on the organization and program committees of international conferences including ACM SIGMOD, ACM SIGKDD, ACM BCB, VLDB, ICDE, EDBT, ACM CIKM, IEEE ICDM, SIAM DM, SSDBM, RECOMB, BIBM. She was elected to the Board of Directors of the ACM Special Interest Group on Bioinformatics, Computational Biology, and Biomedical Informatics (SIGBio) in 2015, and the steering committee of the IEEE Big Data Conference in 2017.

报告题目:Systematic Modeling of Dynamic Networks

摘要:Temporal networks have become ubiquitous because of the numerous applications that generate network structures in a time-dependent way. In recent years, a significant amount of work has been done on the area of evolutionary network analysis, which examines various problems in the context of network evolution, including but not limited to anomaly detection, link prediction, node classification. Although many individual solutions exist for these problems, a broader question is whether we can directly characterize the structure of the network as a function of time. The ability to characterize the structure of the network as a function of time is crucial in using it in different application settings, because such a characterization can capture very rich information about the structure of the underlying network. In this talk, I will present challenges facing dynamic network modeling and our solutions.

Hu Xiangen

Dr. Xiangen Hu is a professor in the Department of Psychology, Department of Electrical and Computer Engineering and Computer Science Department at The University of Memphis (UofM) and senior researcher at the Institute for Intelligent Systems (IIS) at the UofM and is professor and Dean of the School of Psychology at Central China Normal University (CCNU). Dr. Hu received his MS in applied mathematics from Huazhong University of Science and Technology, MA in social sciences and Ph.D. in Cognitive Sciences from the University of California, Irvine. Dr. Hu is the Director of Advanced Distributed Learning (ADL) Partnership Laboratory at the UofM, and is a senior researcher in the Chinese Ministry of Education’s Key Laboratory of Adolescent Cyber-psychology and Behavior. Dr. Hu's primary research areas include Mathematical Psychology, Research Design and Statistics, and Cognitive Psychology. More specific research interests include General Processing Tree (GPT) models, categorical data analysis, knowledge representation, computerized tutoring, and advanced distributed learning. Dr. Hu has received funding for the above research from the US National Science Foundation (NSF), US Institute of Education Sciences (IES), ADL of the US Department of Defense (DoD), US Army Medical Research Acquisition Activity (USAMRAA), US Army Research Laboratories (ARL), US Office of Naval Research (ONR), UofM, and CCNU.

报告题目:Semantic Representation & Analysis (SRA) and potential applications

摘要:Semantic Representation Analysis (SRA) is a general framework for vector-based semantic analysis. Within this framework, semantics of natural language are represented in the form of Induced Semantic Structure (ISS). SRA has applications in information retrieval (IR), text analysis, and intelligent tutoring systems (ITS). In this lecture, I will 1) introduce a mathematical model of SRA; 2) introduce and demonstrate a method that generates individualized domain-specific context sensitive semantic representation; 3) introduce and demonstrate learner’s characteristics curves (LCC) as local student’s model and its application in intelligent tutoring systems.

ShiChuan

石川,博士、北京邮电大学计算机学院教授、博士研究生导师、智能通信软件与多媒体北京市重点实验室副主任。主要研究方向: 数据挖掘、机器学习、人工智能和演化计算。近五年来,作为第一作者或通信作者发表高水平学术论文40余篇,英文专著一部,包括数据挖掘领域的顶级期刊和会议IEEE TKDE、ACM TIST、KAIS、DKE、KDD、SDM、EDBT、ECML、CIKM等。获得ADMA2011国际会议最佳论文奖、CCF-腾讯犀牛鸟基金及项目优秀奖,并指导学生获得顶尖国际数据挖掘竞赛IJCAI Contest 2015 全球冠军。获得北京市高等学校青年英才计划支持。

报告题目:异质信息网络建模与分析

摘要:当前的社会网络分析主要针对同质网络(即网络中结点类型相同),但是现实世界中的网络化数据通常包含不同类型的对象,并且对象之间的关联表示不同的语义关系。构建异质信息网络(即包含不同类型的结点或边的网络)可以包含更加完整的对象之间的关联信息,因此分析这类网络有希望挖掘更加准确的模式。本课题以异质信息网络为对象,深入分析异质网络的复杂结构和丰富语义对数据挖掘带来的挑战。本报告将介绍异质信息网络的基本概念、特点、和分析方法,以及在实际问题中的应用。

CuiPeng

Peng Cui is an Associate Professor in Tsinghua University. He got his PhD degree from Tsinghua University in 2010. He is keen to promote the convergence of social media data mining and multimedia computing technologies. His research interests include network representation learning, human behavioral modeling, and social-sensed multimedia computing. He has published more than 60 papers in prestigious conferences and journals in data mining and multimedia. His recent research won the SIGKDD 2016 Best Paper Finalist, ICDM 2015 Best Student Paper Award, SIGKDD 2014 Best Paper Finalist, IEEE ICME 2014 Best Paper Award, ACM MM12 Grand Challenge Multimodal Award, and MMM13 Best Paper Award. He is the Area Chair of ICDM 2016, ACM MM 2014-2015, IEEE ICME 2014-2015, ICASSP 2013, Associate Editors of IEEE TKDE, ACM TOMM, Elsevier Journal on Neurocomputing, and Guest Editors of IEEE Intelligent Systems, Information Retrieval Journal, Machine Vision and Applications, etc. He was the recipient of ACM China Rising Star Award in 2015.

报告题目:Network Embedding: Enabling Network Analytics and Inference in Vector Space

摘要:Nowadays, larger and larger networks are used in applications. It is well recognized that network data is sophisticated and challenging. To process graph data effectively, the first critical challenge is network data representation, that is, how to represent networks properly so that advanced analytic tasks, such as pattern discovery, analysis and prediction, can be conducted efficiently in both time and space. In this talk, we will review the recent thoughts and achievements on network embedding. More specifically, a series of fundamental problems in network embedding will be discussed, including why we need to revisit network representation, what are the research goals of network embedding, how network embedding can be learned, and the major future directions of network embedding.

刘知远

刘知远,清华大学计算机系助理教授。主要研究方向为表示学习、知识图谱和社会计算。已在人工智能领域著名国际期刊和会议发表相关论文30余篇,Google Scholar统计引用超过2000次。曾获清华大学优秀博士学位论文、中国人工智能学会优秀博士学位论文、清华大学优秀博士后、中文信息学会青年创新奖,入选中国科学青年人才托举工程、CCF-Intel青年学者提升计划。

报告题目:语言表示学习与计算社会科学

摘要:语言是人类交流的工具、人类文化的载体,是了解人类社会的重要视角。近年来随着表示学习在自然语言处理中的应用,语言表示学习也为社会科学研究提供了全新的技术工具,特别是面向在线社会媒体的大规模用户产生内容进行用户和内容分析,具有很大优势。本报告将介绍语言表示学习技术在计算社会科学方面的最新动态,探讨该方向的未来发展趋势

HanJiawei

Jiawei Han is Abel Bliss Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign.   He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 900 journal and conference publications. He has chaired or served on many program committees of international conferences in most data mining and database conferences. He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and the Director of Information Network Academic Research Center supported by U.S. Army Research Lab (2009-2016), and is the co-Director of KnowEnG, an NIH funded Center of Excellence in Big Data Computing since 2014. He is Fellow of ACM, Fellow of IEEE, and received 2004 ACM SIGKDD Innovations Award, 2005 IEEE Computer Society Technical Achievement Award, and 2009 M. Wallace McDowell Award from IEEE Computer Society. His co-authored book "Data Mining: Concepts and Techniques" has been adopted as a textbook popularly worldwide.

报告题目:Multi-Dimensional Analysis of Massive Text Corpora, Jiawei Han

摘要:The real-world big data are largely unstructured and interconnected, in the form of natural language text. It is highly desirable to view and analyze massive text data from multi-dimensional angles. This poses a major challenge on how to transform unstructured text data into structured text and analyze such data in multidimensional space. To facilitate such analytical functionality, we propose a textcube modeling and discuss how to construct such cubes from massive text corpora and how to conduct multidimensional OLAP analysis using such textcubes. In the past several years, we have developed a text mining approach that only needs distant or minimal supervision but relies on massive data. We show (i) quality phrases can be mined from such massive text data, (ii) types can be extracted from massive text data with distant supervision, (iii) entities, attributes and values can be discovered by meta-path directed pattern discovery, (iv) faceted taxonomy can be constructed from massive corpora, (v) textcubes can be constructed from massive text, and (v) multi-dimensional analysis can be conducted on such cubes. We show such a paradigm represents a promising direction on turning massive text data into structured and useful knowledge.

赵鑫

赵鑫,中国人民大学计算机副教授。研究领域为社交数据挖掘和自然语言处理,共发表CCF A/B、SCI论文50余篇,其中以第一作者发表的《Comparing Twitter and Traditional Media Using Topic Models》被引用700余次。入选第二届CCF青年人才发展计划。担任多个国际顶级期刊和学术会议评审,AIRS 2016出版主席、SMP 2017领域主席以及NLPCC 2017领域主席。

报告题目:面向社交媒体平台的商业数据挖掘

摘要:随着互联网技术的不断发展,各种社交媒体平台都得到了广泛的使用。社交网络平台中蕴含大量的用户信息,包括用户个人属性信息(如年龄、性别等等)、用户所发表的内容信息等等。如何充分利用社交媒体平台的信息来加强用户个性化建模,从而推动商业数据挖掘成了一个研究热点。本次报告试图系统梳理一些重要的商业大数据应用问题,如用户意图检测、用户画像构建以及推荐算法等。

杨洋

Yang Yang is an assistant professor at College of Computer Science and Technology, Zhejiang University. His research focuses on mining deep knowledge from large-scale social and information networks. He obtained his Ph.D. degree from Tsinghua University in 2016, advised by Jie Tang and Juanzi Li. He has published over 20 papers in top conference/journals such as KDD, AAAI, TKDD, ICDM, etc. He has been visiting Cornell University (working with John Hopcroft) in 2012, and University of Leuven (working with Marie-Francine Moens) in 2013. He served as PC members in WWW’17, WSDM’16’17, CIKM’16’17, ICWSM’17, and ASONAM’15.

报告题目:Urban Dreams of Migrants: A Case Study of Migrant Integration in Shanghai

摘要:An unprecedented human mobility has driven the rapid urbanization around the world. In China, the fraction of population dwelling in cities increased from 17.9% to 52.6% between 1978 and 2012. Such large-scale migration poses both significant challenges for policymakers and important questions for researchers. In this talk, I will introduce our study of the process of migrant integration. Specifically, we employ a one-month complete dataset of telecommunication metadata in Shanghai with 54 million users and 698 million call logs. We find systematic differences between locals and migrants in their mobile communication networks and geographical locations. For instance, migrants have more diverse contacts and move around the city with a larger radius than locals after they settle down. By distinguishing new migrants (who recently moved to Shanghai) from settled migrants (who have been in Shanghai for a while), we demonstrate the integration process of new migrants in their first three weeks. Moreover, we further investigate migrants’ behavior in their first weeks and in particular, how their behavior relates to early departure. We find that migrants who end up leaving early tend to neither develop diverse connections in their first weeks nor move around the city. Their active areas also have higher housing prices than that of staying migrants.

学术主任:

Tangjie

唐杰,清华大学计算机系长聘副教授、博导,首届国家优秀青年基金获得者,CCF青年科学家,英国牛顿高级学者奖。主要研究社会网络分析、数据挖掘和机器学习。发表200余篇论文,Google引用9400余次。研发了研究者社会网络ArnetMiner系统,吸引全球220个国家和地区832万独立IP的访问。获北京市科技进步一等奖、中国人工智能学会科技进步一等奖。

刘知远

刘知远,清华大学计算机系助理教授。主要研究方向为表示学习、知识图谱和社会计算。已在人工智能领域著名国际期刊和会议发表相关论文30余篇,Google Scholar统计引用超过2000次。曾获清华大学优秀博士学位论文、中国人工智能学会优秀博士学位论文、清华大学优秀博士后、中文信息学会青年创新奖,入选中国科学青年人才托举工程、CCF-Intel青年学者提升计划

日程安排:

2017年12月22日(周五)
08:30-09:00 开班仪式

09:00-09:15   合影
09:15-12:15 专题讲座1:Broad Learning via Fusion of Social Network Information

                      Philip S. Yu,美国伊利诺伊大学芝加哥分校特聘教授,清华大学数科院院长,ACM/IEEE会士

                      专题讲座2:社交影响力与行为预测

                      唐杰,清华大学副教授

12:30-14:00   午餐
14:00-16:00   专题讲座3:在线社交媒体中的信息传播预测

                      沈华伟,中科院计算所研究员

                      专题讲座4:社会网络信息传播影响最大化挖掘

                      宋国杰,北京大学副教授

16:00-17:30   Panel:社交大数据带来的机遇和挑战

2017年12月23日(周六)

09:00-12:00   专题讲座5:Systematic Modeling of Dynamic Networks

                      Wei Wang,UCLA教授、KDD 2016 Service Award

                      专题讲座6:Semantic Representation & Analysis (SRA) and potential applications

                       Xiangen Hu,千人、孟菲斯大学教授、华中师范大学教授、院长

12:00-14:00   午餐

14:00-17:00   专题讲座7:异质信息网络建模与分析

                      石川,北京邮电大学教授

                      专题讲座8:

                      Network Embedding: Enabling Network Analytics and Inference in Vector Space

                      崔鹏,清华大学副教授

                      专题讲座9:语言表示学习与计算社会科学

                      刘知远,清华大学助理教授

2017年12月24日(周日)
09:00-12:00   专题报告10:Multi-Dimensional Analysis of Massive Text Corpora

                       Jiawei Han,UIUC教授、ACM/IEEE Fellow

12:00-14:00   午餐

14:00-16:00   专题讲座11:面向社交媒体平台的商业数据挖掘

                      赵鑫,中国人民大学副教授

                      专题讲座12:

                      Urban Dreams of Migrants: A Case Study of Migrant Integration in Shanghai

                      杨洋,浙江大学助理教授

16:00-17:00   Panel: 社交大数据挖掘的应用前景

                      结业式

(如有变动,以现场为准)

地点:中科院计算所一层报告厅(北京市海淀区中关村科学院南路6号)

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