中國中文信息學會社會媒體處理專委會（SMP）主辦的全國社會媒體處理大會，是全國社會媒體處理領域的旗艦會議,，每年有數(shù)百位來自學界和業(yè)界的同仁注冊參會,，在國內(nèi)產(chǎn)生了非常良好的影響,。為進一步推進計算科學和社會科學的交叉融合，迸發(fā)出更多更好的思想火花以及促進研究成果的落地,，SMP專委會從2017年開始推出全國社會媒體處理講習班（SMP Tutorials），旨在選擇計算科學和社會科學交叉融合的重點領域和關鍵技術進行系統(tǒng)深入的講解,。講習班的講者包括領域大咖和一線青年骨干,，講習班本著梳理脈絡,、引領方向、探索未來的思路組織,，以冀為社會科學和計算科學的交叉融合提供新動力和新思潮,。

        首屆全國社會媒體處理講習班將于2017年9月14-15日在北京友誼賓館舉辦。講習班第一天為社會科學專場,，邀請了社會科學領域著名學者中山大學梁玉成講授,、北京師范大學的張倫博士和南京大學王成軍博士，介紹計算社會學和計算傳播學的研究進展,。講習班的第二天為計算科學專場,，邀請了社會媒體計算和數(shù)據(jù)挖掘領域的青年才俊微軟亞洲研究院的唐建博士和清華大學的崔鵬博士，介紹網(wǎng)絡表示學習方面的最新研究進展,。

歡迎廣大老師和同學們注冊參加,！

首屆SMP講習班報名信息

時間：2017年9月14（周四）-15日（周五）

地點：北京友誼賓館

主頁：http://www.cips-smp.org/smp2017/public/tutorial.html

在線報名現(xiàn)已開放：

http://www.cips-smp.org/smp2017/public/register.html

注冊費：（含資料費和午餐費）

早注冊費（9月1日前）：1200元

學生：800元

同時注冊SMP Tutorials和SMP主會將有20%的折扣。

講習班專題及講者簡介

專題（一）：計算社會學的理論與方法

報告摘要：不同于傳統(tǒng)社會科學所依賴的調(diào)查問卷,，來自社交網(wǎng)絡的電子行為蹤跡呈現(xiàn)了微觀,，異質(zhì)，實時,，大規(guī)模,，和相互關聯(lián)等特征。在此基礎之上,，基于互聯(lián)網(wǎng)的大數(shù)據(jù),，以及傳統(tǒng)的問卷調(diào)查與行政大數(shù)據(jù)結合，都成為新的研究平臺,，幫助學者來認識從人類行為和社會原理,。計算社會科學屬跨學科的新領域。許多重要的工作來自計算機科學,，物理學和數(shù)學,。我將介紹這些跨學科的方法，主要包括傳統(tǒng)調(diào)查數(shù)據(jù)與大數(shù)據(jù)結合的法則,、跨越社會宏觀與微觀結構的社會計算,、基于文本數(shù)據(jù)的社會理論研究等。

特邀講者：中山大學 國家治理研究院副院長  梁玉成  教授

講者簡介：中山大學物理學學士,、社會學碩士,；香港科技大學社會學博士；約翰霍普金斯大學社會學系訪問教授,。目前系中山大學社會學系教授,，國家治理研究院副院長，社會科學調(diào)查中心主任，主持國家社科重大課題等課題10多項,。獲得教育部優(yōu)秀社科成果獎二等獎,、三等獎各一次；廣東省優(yōu)秀社科成果一等獎一次,。目前系中山大學計算社會科學大團隊負責人。研究領域包括社會調(diào)查,、基于政府行政大數(shù)據(jù)的社會治理等,。

專題（二）：計算社會科學視角下的計算傳播學
報告摘要：基因是生物學飛躍的原因，貨幣是經(jīng)濟學發(fā)展的關鍵,。人類傳播行為所隱藏的計算化“基因”是什么,？計算傳播學是計算社會科學的重要分支。它致力于尋找傳播學可計算化的基因,，以傳播網(wǎng)絡分析,、傳播文本挖掘、數(shù)據(jù)科學等為主要分析工具,，大規(guī)模地收集并分析人類傳播行為數(shù)據(jù),，挖掘人類傳播行為背后的模式和法則，分析模式背后的生成機制與基本原理,，可以被廣泛地應用于數(shù)據(jù)新聞和計算廣告等場景,。注重編程訓練、數(shù)學建模與計算思維,。本次講座將介紹計算傳播學的概念,、內(nèi)涵、應用,、工具,，并討論如何開展跨學科合作、計算傳播學的研究策略等問題,。

特邀講者：南京大學新聞傳播學院  王成軍  副教授   

 
講者簡介：王成軍,，傳播學博士。現(xiàn)為南京大學新聞傳播學院副教授,，奧美數(shù)據(jù)科學實驗室主任,，計算傳播學實驗中心副主任。參與翻譯《社會網(wǎng)絡分析：方法與實踐》(2013),、合著《社交網(wǎng)絡上的計算傳播學》(2015),。其研究興趣聚焦于采用計算社會科學視角分析人類傳播行為，研究成果發(fā)表于SSCI和SCI索引的期刊,，例如Scientific Reports,、PloS ONE、Physica A、Cyberpsychology,。2014年,，發(fā)起創(chuàng)建計算傳播網(wǎng) computational-communication.com。

特邀講者：北京師范大學藝術與傳媒學院  張倫  副教授

講者簡介：張倫,，傳播學博士,，北京師范大學數(shù)字媒體系副教授。主要研究方向為基于數(shù)據(jù)挖掘方法的新媒體信息傳播,，即以傳播網(wǎng)絡分析,、傳播文本挖掘、數(shù)據(jù)科學等為主要分析工具,大規(guī)模地收集并分析人類傳播行為數(shù)據(jù),，挖掘人類傳播行為背后的模式和法則,分析模式背后的生成機制與基本原理,。于SSCI、SCI以及CSSCI索引期刊發(fā)表論文18篇,，其中SSCI期刊論文5篇,，SCI期刊論文1篇，CSSCI期刊論文12篇,。合著出版《社交網(wǎng)絡上的計算傳播學》（高等教育出版社,， 2015年）一書。

專題（三）：Learning Representations of Large-scale Networks
報告摘要：Large-scale networks such as social networks, citation networks, the World Wide Web, and traffic networks are ubiquitous in the real world. Networks can also be constructed from text, time series, behavior logs, and many other types of data. Mining network data attracts increasing attention in academia and industry, covers a variety of applications, and influences the methodology of mining many types of data. A prerequisite to network mining is to find an effective representation of networks, which largely determines the performance of downstream data mining tasks. Traditionally, networks are usually represented as adjacency matrices, which suffer from data sparsity and high-dimensionality. Recently, there is a fast-growing interest in learning continuous and low-dimensional representations of networks. This is a challenging problem for multiple reasons: (1) networks data (nodes and edges) are sparse, discrete, and globally interactive; (2) real-world networks are very large, usually containing millions of nodes and billions of edges; and (3) real-world networks are heterogeneous. Edges can be directed, undirected or weighted, and both nodes and edges may carry different semantics. 

In this tutorial, we will introduce the recent progress on learning continuous and low-dimensional representations of large-scale networks. This includes methods that learn the embeddings of nodes, methods that learn representations of larger graph structures (e.g., an entire network), and methods that layout very large networks on extremely low (2D or 3D) dimensional spaces. We will introduce methods for learning different types of node representations: representations that can be used as features for node classification, community detection, link prediction, and network visualization. We will introduce end-to-end methods that learn the representation of the entire graph structure through directly optimizing tasks such as information cascade prediction, chemical compound cl

特邀講者：HEC Montreal & MILA  Jian Tang  Ph.D

講者簡介：Dr. Jian Tang will be joining the department of decision science, HEC Montreal, as an assistant professor starting from this fall. He will also be a faculty member of Montreal Institute for Learning Algorithms (MILA), which is the deep learning group lead by one of the deep learning pioneers Yoshua Bengio. His research interests are deep learning, reinforcement learning, statistical topic modelling with various applications. He was a research fellow in University of Michigan and Carnegie Mellon University. He received his Ph.D degree from Peking University and was an associate researcher in Microsoft Research Asia. He received the best paper award of ICML’14 and nominated for the best paper of WWW’16. He is a PC member of many prestigious conferences such as IJCAI, AAAI, ACL, EMNLP, WWW, WSDM, and KDD.

專題（四）：Network Embedding: Enabling Network Analytics and Inference in Vector Space

報告摘要：Nowadays, larger and larger, more and more sophisticated networks are used in more and more 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 tutorial, 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.

特邀講者：Tsinghua University  Peng Cui  Associate Professor

講者簡介：Peng Cui is an Associate Professor in Tsinghua University. He got his PhD degree from Tsinghua University in 2010. His research interests include network representation learning, social dynamics modeling and human behavioral modeling. He has published more than 60 papers in prestigious conferences and journals in data mining and multimedia. His recent research won the 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 Editor of IEEE TKDE, ACM TOMM, Elsevier Journal on Neurocomputing. He was the recipient of ACM China Rising Star Award in 2015. More details.