中國(guó)中文信息學(xué)會(huì)社會(huì)媒體處理專(zhuān)委會(huì)（SMP）主辦的全國(guó)社會(huì)媒體處理大會(huì),，是全國(guó)社會(huì)媒體處理領(lǐng)域的旗艦會(huì)議,，每年有數(shù)百位來(lái)自學(xué)界和業(yè)界的同仁注冊(cè)參會(huì)，在國(guó)內(nèi)產(chǎn)生了非常良好的影響,。為進(jìn)一步推進(jìn)計(jì)算科學(xué)和社會(huì)科學(xué)的交叉融合,，迸發(fā)出更多更好的思想火花以及促進(jìn)研究成果的落地，SMP專(zhuān)委會(huì)從2017年開(kāi)始推出全國(guó)社會(huì)媒體處理講習(xí)班（SMP Tutorials）,，旨在選擇計(jì)算科學(xué)和社會(huì)科學(xué)交叉融合的重點(diǎn)領(lǐng)域和關(guān)鍵技術(shù)進(jìn)行系統(tǒng)深入的講解,。講習(xí)班的講者包括領(lǐng)域大咖和一線(xiàn)青年骨干，講習(xí)班本著梳理脈絡(luò),、引領(lǐng)方向,、探索未來(lái)的思路組織，以冀為社會(huì)科學(xué)和計(jì)算科學(xué)的交叉融合提供新動(dòng)力和新思潮,。

        首屆全國(guó)社會(huì)媒體處理講習(xí)班將于2017年9月14-15日在北京友誼賓館舉辦,。講習(xí)班第一天為社會(huì)科學(xué)專(zhuān)場(chǎng),，邀請(qǐng)了社會(huì)科學(xué)領(lǐng)域著名學(xué)者中山大學(xué)梁玉成講授、北京師范大學(xué)的張倫博士和南京大學(xué)王成軍博士,，介紹計(jì)算社會(huì)學(xué)和計(jì)算傳播學(xué)的研究進(jìn)展,。講習(xí)班的第二天為計(jì)算科學(xué)專(zhuān)場(chǎng)，邀請(qǐng)了社會(huì)媒體計(jì)算和數(shù)據(jù)挖掘領(lǐng)域的青年才俊微軟亞洲研究院的唐建博士和清華大學(xué)的崔鵬博士,，介紹網(wǎng)絡(luò)表示學(xué)習(xí)方面的最新研究進(jìn)展,。

歡迎廣大老師和同學(xué)們注冊(cè)參加！

首屆SMP講習(xí)班報(bào)名信息

時(shí)間：2017年9月14（周四）-15日（周五）

地點(diǎn)：北京友誼賓館

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

在線(xiàn)報(bào)名現(xiàn)已開(kāi)放：

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

注冊(cè)費(fèi)：（含資料費(fèi)和午餐費(fèi)）

早注冊(cè)費(fèi)（9月1日前）：1200元

學(xué)生：800元

同時(shí)注冊(cè)SMP Tutorials和SMP主會(huì)將有20%的折扣,。

講習(xí)班專(zhuān)題及講者簡(jiǎn)介

專(zhuān)題（一）：計(jì)算社會(huì)學(xué)的理論與方法

報(bào)告摘要：不同于傳統(tǒng)社會(huì)科學(xué)所依賴(lài)的調(diào)查問(wèn)卷,，來(lái)自社交網(wǎng)絡(luò)的電子行為蹤跡呈現(xiàn)了微觀(guān)，異質(zhì),，實(shí)時(shí),，大規(guī)模，和相互關(guān)聯(lián)等特征,。在此基礎(chǔ)之上,，基于互聯(lián)網(wǎng)的大數(shù)據(jù)，以及傳統(tǒng)的問(wèn)卷調(diào)查與行政大數(shù)據(jù)結(jié)合,，都成為新的研究平臺(tái),，幫助學(xué)者來(lái)認(rèn)識(shí)從人類(lèi)行為和社會(huì)原理。計(jì)算社會(huì)科學(xué)屬跨學(xué)科的新領(lǐng)域,。許多重要的工作來(lái)自計(jì)算機(jī)科學(xué),，物理學(xué)和數(shù)學(xué)。我將介紹這些跨學(xué)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(jié)合的法則,、跨越社會(huì)宏觀(guān)與微觀(guān)結(jié)構(gòu)的社會(huì)計(jì)算、基于文本數(shù)據(jù)的社會(huì)理論研究等,。

特邀講者：中山大學(xué) 國(guó)家治理研究院副院長(zhǎng)  梁玉成  教授

講者簡(jiǎn)介：中山大學(xué)物理學(xué)學(xué)士,、社會(huì)學(xué)碩士；香港科技大學(xué)社會(huì)學(xué)博士,；約翰霍普金斯大學(xué)社會(huì)學(xué)系訪(fǎng)問(wèn)教授,。目前系中山大學(xué)社會(huì)學(xué)系教授，國(guó)家治理研究院副院長(zhǎng),，社會(huì)科學(xué)調(diào)查中心主任,，主持國(guó)家社科重大課題等課題10多項(xiàng)。獲得教育部?jī)?yōu)秀社科成果獎(jiǎng)二等獎(jiǎng),、三等獎(jiǎng)各一次,；廣東省優(yōu)秀社科成果一等獎(jiǎng)一次。目前系中山大學(xué)計(jì)算社會(huì)科學(xué)大團(tuán)隊(duì)負(fù)責(zé)人,。研究領(lǐng)域包括社會(huì)調(diào)查,、基于政府行政大數(shù)據(jù)的社會(huì)治理等,。

專(zhuān)題（二）：計(jì)算社會(huì)科學(xué)視角下的計(jì)算傳播學(xué)
報(bào)告摘要：基因是生物學(xué)飛躍的原因，貨幣是經(jīng)濟(jì)學(xué)發(fā)展的關(guān)鍵,。人類(lèi)傳播行為所隱藏的計(jì)算化“基因”是什么？計(jì)算傳播學(xué)是計(jì)算社會(huì)科學(xué)的重要分支,。它致力于尋找傳播學(xué)可計(jì)算化的基因,，以傳播網(wǎng)絡(luò)分析、傳播文本挖掘,、數(shù)據(jù)科學(xué)等為主要分析工具,，大規(guī)模地收集并分析人類(lèi)傳播行為數(shù)據(jù)，挖掘人類(lèi)傳播行為背后的模式和法則,，分析模式背后的生成機(jī)制與基本原理,，可以被廣泛地應(yīng)用于數(shù)據(jù)新聞和計(jì)算廣告等場(chǎng)景。注重編程訓(xùn)練,、數(shù)學(xué)建模與計(jì)算思維,。本次講座將介紹計(jì)算傳播學(xué)的概念、內(nèi)涵,、應(yīng)用,、工具，并討論如何開(kāi)展跨學(xué)科合作,、計(jì)算傳播學(xué)的研究策略等問(wèn)題,。

特邀講者：南京大學(xué)新聞傳播學(xué)院  王成軍  副教授   

 
講者簡(jiǎn)介：王成軍，傳播學(xué)博士?，F(xiàn)為南京大學(xué)新聞傳播學(xué)院副教授,，奧美數(shù)據(jù)科學(xué)實(shí)驗(yàn)室主任，計(jì)算傳播學(xué)實(shí)驗(yàn)中心副主任,。參與翻譯《社會(huì)網(wǎng)絡(luò)分析：方法與實(shí)踐》(2013),、合著《社交網(wǎng)絡(luò)上的計(jì)算傳播學(xué)》(2015)。其研究興趣聚焦于采用計(jì)算社會(huì)科學(xué)視角分析人類(lèi)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(jì)算傳播網(wǎng) computational-communication.com,。

特邀講者：北京師范大學(xué)藝術(shù)與傳媒學(xué)院  張倫  副教授

講者簡(jiǎn)介：張倫，傳播學(xué)博士,，北京師范大學(xué)數(shù)字媒體系副教授,。主要研究方向?yàn)榛跀?shù)據(jù)挖掘方法的新媒體信息傳播,，即以傳播網(wǎng)絡(luò)分析、傳播文本挖掘,、數(shù)據(jù)科學(xué)等為主要分析工具,大規(guī)模地收集并分析人類(lèi)傳播行為數(shù)據(jù),，挖掘人類(lèi)傳播行為背后的模式和法則,分析模式背后的生成機(jī)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(luò)上的計(jì)算傳播學(xué)》（高等教育出版社， 2015年）一書(shū),。

專(zhuān)題（三）：Learning Representations of Large-scale Networks
報(bào)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(jiǎn)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(zhuān)題（四）：Network Embedding: Enabling Network Analytics and Inference in Vector Space

報(bào)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(jiǎn)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.