中国计算机学会青年计算机科技论坛

CCF Young Computer Scientists&Engineers Forum

CCF YOCSEF深圳

时间：2017年5月12日（星期五）下午 2:30-4:30

地点：腾讯大厦411

学术报告会

主题

Graph degeneracy and applications to social networks and text mining

主办单位

CCFYOCSEF深圳分论坛

腾讯科技（深圳）有限公司

执行主席

张 龙CCF YOCSEF深圳AC委员

毛 睿 CCF YOCSEF深圳现任主席

雷 凯 CCF YOCSEF深圳候任主席

机器学习、图理论和文本挖掘是备受关注且具备广泛应用价值的领域，本期学术报告会CCF YOCSEF深圳与腾讯共同举办，并邀请到了此领域的顶级专家-巴黎综合理工学院的Prof. Vazirgiannis，他与众多顶尖高校及科研机构有非常密切的联合科研合作，他会与我们分享关于退化图研究及其在社交网络、文本挖掘中的应用。

程序

14:20签到

14:30-16:30 报告题目： Graph degeneracy and applications to social networks and text mining

    特邀嘉宾：Dr. Michalis Vazirgiannis

16:30-17:00 提问与自由讨论时间

演讲报告： 

Graph degeneracy and applications to social networks and text mining

嘉宾简介： 

Dr. Vazirgiannis is the head of the DaSciM research team on Data Science & Web Mining. He holds a degree in Physics and a PhD in Informatics from Athens University(Greece) and a Master degree in AI from HerioWatt Univ Edinburgh.

His current research interests are on machine learning and combinatorial methods for Graph analysis, Text mining including Graph of Words, word embeddings with applications to web advertising and marketing, event detection and summarization. Also distributed machine learning algorithms, distributed dimensionality reduction, distributed resource management.

报告摘要： 

Graph degeneracy is a popular method to approximate the densest subgraph in almost linear complexity time. In our research work we extended this method to weighted and directed graphs and capitalizing on them to investigate its potential in different graph and text mining cases. We also investigate thoroughly the issue of graph similarity via novel graph kernels and embedding schemes with applications to graph classification in chemo-informatics, social networks and text mining. At the level of Text mining, we capitalize on the Graph-of Words (GoW) model. We applied graph-of-word in various tasks such as ad-hoc Information Retrieval, Single-Document Keyword Extraction, Text Categorization and Sub-event Detection in Textual Streams (i.e. twitter) and document summarization. In all cases the graph of word approach, assisted by degeneracy at times, outperforms the state of the art base lines in all cases. We are currently investigating the potential of the GoW as input to deep learning architectures for text mining tasks.