Machine-Learning-Aided Social Networking


Machine-Learning-Aided Social Networking

8:30 AM — 12:00 PM CST
Jul 25 Sun, 8:30 PM — 12:00 AM EDT

Graph Ricci Flow and Applications in Network Analysis and Learning

Jie Gao (Rutgers University)

The notion of curvature describes how spaces are bent at each point and Ricci flow deforms the space such that curvature changes in a way analogous to the diffusion of heat. In this talk I will discuss some recent work in my group on discrete Ollivier Ricci curvature defined on graphs. Discrete curvature defined on an edge captures the local connectivity in the neighborhood. In general edges within a densely connected community have positive curvature while edges connecting different communities have negative curvature. By deforming edge weights with respect to curvature one can derive a Ricci flow metric which is robust to edge insertion/deletion.I will show applications of graph Ricci flow in graph analysis and learning, including network alignment, community detection and graph neural networks.

Bio: Professor Jie Gao is currently Professor in computer science, Rutgers
University. She was on the faculty of Computer Science department at Stony
Brook University from 2005-2019. She received B.Eng from the Special Class
of the Gifted Young, University of Science and Technology of China in 1999,
Ph.D in Computer Science from Stanford University in 2004 and a postdoc at
Caltech from 2004-2005. She received the NSF career award in 2006, IMC best
paper award (2009), EWSN best paper award (2021) and multiple Research
Excellence Award in computer science department of Stony Brook. She is
currently serving on the editorial board of ACM Transactions on Sensor
Networks and International Journal of Computational Geometry and
Applications. She published over 140 referred papers in computer networking
and theoretical computer science fields, and has graduated over 16 Ph.D

Forward and Inverse Problems in Combating Fake News

Lei Ying (University of Michigan, Ann Arbor)

The proliferation of fake news on online social networks has eroded the public trust in news media and has become an imminent threat to the ecosystem of online social platforms like Facebook, Twitter, and Sina Weibo. This talk will review some forward and inverse problems in combating fake news, and will discuss two fundamental questions: (i) how to locate the source of fake news with partial observations? and (ii) how to quickly detect fake news at its early stage before it becomes viral?

Bio: Lei Ying received his B.E. degree from Tsinghua University, Beijing, China, and his M.S. and Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. He currently is a Professor at the Electrical Engineering and Computer Science Department of the University of Michigan, Ann Arbor, and an Associate Editor of the IEEE Transactions on Information Theory. His research is broadly in the interplay of complex stochastic systems and big data, including large-scale communication/computing systems for big-data processing, private data marketplaces, and large-scale graph mining. He co-authored books Communication Networks: An Optimization, Control, and Stochastic Networks Perspective, Cambridge University Press, 2014; and Diffusion Source Localization in Large Networks, Synthesis Lectures on Communication Networks, Morgan & Claypool Publishers, 2018. He won the Young Investigator Award from the Defense Threat Reduction Agency (DTRA) in 2009 and the NSF CAREER Award in 2010. my research contributions have been recognized as the best papers in conferences across different disciplines, including communication networks (INFOCOM and WiOpt), computer systems (SIGMETRICS), and data mining (KDD).

Pushing the Limit of IoT Sensing : Intelligent and Secure Sensing Driven Design

Qian Zhang (Hong Kong University of Science and Technology)

The IoT ecosystem consists of three parties: Internet-of-Thing systems, physical world and adversaries. We believe that a well-functioning IoT system should satisfy three basic requirements. Firstly, IoT devices should be able to properly interact with and intelligently sense the physical world. Secondly, IoT devices should be able to identify other authentic IoT peers to enable device cooperation. Thirdly, IoT system should be robust against spoo?ng from adversaries.
In this talk, I would like to share some of our recent efforts on intelligent and secure IoT sensing by examining the above requirements. Particularly, I would like to share some of work related to contactless sensing as well as some potential spoofing attack that may exist in the smart sensing scenario.

Bio: Dr. Zhang joined Hong Kong University of Science and Technology in Sept. 2005 where she is now Tencent Professor of Engineering and Chair Professor of the Department of Computer Science and Engineering. She is also serving as the co-director of Huawei-HKUST innovation lab and the director of digital life research center of HKUST. Before that, she was in Microsoft Research Asia, Beijing, from July 1999, where she was the research manager of the Wireless and Networking Group. Dr. Zhang has published more than 400 refereed papers in international leading journals and key conferences. She is the inventor of more than 50 granted International patents. Her current research interests include Internet of Things (IoT), smart health, mobile computing and sensing, wireless networking, as well as cyber security.

She is a Fellow of the IEEE and the Hong Kong Academy of Engineering Science (HKAES). Dr. Zhang has received MIT TR100 (MIT Technology Review) world's top young innovator award. She also received the Best Asia Pacific (AP) Young Researcher Award elected by IEEE Communication Society in year 2004. She received the Best Paper Awards in several international conferences. She received the Oversea Young Investigator Award from the National Natural Science Foundation of China (NSFC) in 2006. She held the Cheung Kong Chair Professor in Huazhong University of Science and Technology (2012-2015).
Dr. Zhang is serving as Editor-in-Chief of IEEE Trans. on Mobile Computing (TMC). She is a member of Steering Committee of IEEE Infocom.
Dr. Zhang received the B.S., M.S., and Ph.D. degrees from Wuhan University, China, in 1994, 1996, and 1999, respectively, all in computer science.

Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning

John C.S Lui (The Chinese University of Hong Kong)

Multi-layered network exploration (MuLaNE) problem is an important problem abstracted from many applications. In MuLaNE, there are multiple network layers where each node has an importance weight and each layer is explored by a random walk. The MuLaNE task is to allocate total random walk budget B into each network layer so that the total weights of the unique nodes visited by random walks are maximized. We systematically study this problem from offline optimization to online learning. For the offline optimization setting where the network structure and node weights are known, we provide greedy based constant-ratio approximation algorithms for overlapping networks, and greedy or dynamic-programming based optimal solutions for non-overlapping networks. For the online learning setting, neither the network structure nor the node weights are known initially. We adapt the combinatorial multi-armed bandit framework and design algorithms to learn random walk related parameters and node weights while optimizing the budget allocation in multiple rounds, and prove that they achieve logarithmic regret bounds. Finally, we conduct experiments on a real-world social network dataset to validate our theoretical results.

Bio: John C.S. Lui is currently the Choh-Ming Li Chair Professor in the Department of Computer Science & Engineering (CSE) at The Chinese University of Hong Kong (CUHK). His current research interests are in online learning algorithms and applications, quantum networks, machine learning on network sciences and networking systems, large scale data analytics, network/system security, network economics, large scale storage systems and performance evaluation theory. He is a member of the review panel in the IEEE Koji Kobayashi Computers and Communications Award committee, and has served at the IEEE Fellow Review Committees. John has also been a reviewer and panel member for NSF, Canadian Research Council and the National Natural Science Foundation of China (NSFC). John has served as the Associate Dean of Research in the College of Engineering at CUHK and was the chairman of the CSE Department. He received various departmental teaching awards and the CUHK Vice-Chancellor's Exemplary Teaching Award, as well as the CUHK Faculty of Engineering Research Excellence Award. He is an elected member of the IFIP WG 7.3, Fellow of ACM, Fellow of IEEE, Senior Research Fellow of the Croucher Foundation, Fellow of the Hong Kong Academy of Engineering Sciences (HKAES), and RGC Senior Research Fellow.

Optimization and Incentive Mechanism Design for Federated Learning

Jianwei Huang (The Chinese University of Hong Kong, Shenzhen)

As an emerging machine learning paradigm in edge computing, federated learning has received significant attention recently due to its promising performance in mitigating privacy risks and costs. While most of the existing work of federated learning focused on designing learning algorithm to improve computing performance, the incentive issue for encouraging edge devices' participation is under-explored. In this talk, we will present an analytical study on the cloud's optimal incentive mechanism design, in the presence of strategic edge devices' multi-dimensional private information. We will perform the analysis in three information scenarios to reveal the impact of information asymmetry level on the overall system performance.

Bio: Jianwei Huang received the Ph.D. degree in ECE from Northwestern University in 2005, and worked as a Postdoc Research Associate in Princeton University during 2005-2007. From 2007 until 2018, he was on the faculty of Department of Information Engineering, The Chinese University of Hong Kong. Since 2019, he has been on the faulty at The Chinese University of Hong Kong, Shenzhen, where he is currently a Presidential Chair Professor and an Associate Dean of the School of Science and Engineering. He also serves as a Vice President of Shenzhen Institute of Artificial Intelligence and Robotics for Society. His research interests are in the area of network optimization, network economics, and network science, with applications in communication networks, energy networks, data markets, crowd intelligence, and related fields. He has published more than 300 papers in leading venues, with a Google Scholar citation of 13000+ and an H-index of 58. He has co-authored 9 Best Paper Awards, including the 2011 IEEE Marconi Prize Paper Award in Wireless Communications. He has co-authored seven books, including the textbook on "Wireless Network Pricing." He is an IEEE Fellow, and was an IEEE ComSoc Distinguished Lecturer and a Clarivate Web of Science Highly Cited Researcher. He is the Editor-in-Chief of IEEE Transactions on Network Science and Engineering, and was the Associate Editor-in-Chief of IEEE Open Journal of the Communications Society.

A One-Size-Fits-All Solution to Conservative Bandit Problems

Longbo Huang (Tsinghua University)

In this paper, we study a family of conservative bandit problems (CBPs) with sample-path reward constraints, i.e., the learner's reward performance must be at least as well as a given baseline at any time. We propose a general one-sizefits-all solution to CBPs and present its applications to three encompassed problems, i.e., conservative multi-armed bandits (CMAB), conservative linear bandits (CLB) and conservative contextual combinatorial bandits (CCCB). Different from previous works which consider high probability constraints on the expected reward, our algorithms guarantee sample-path constraints on the actual received reward, and achieve better theoretical guarantees (T-independent additive regrets instead of T-dependent) and empirical performance. Furthermore, we extend the results and consider a novel conservative mean-variance bandit problem (MVCBP), which measures the learning performance in both the expected reward and variability. We design a novel algorithm with O(1/T) normalized additive regrets (T-independent in the cumulative form) and validate this result through empirical evaluation.

Bio: Dr. Longbo Huang is an associate professor (with tenure) at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University, Beijing, China. Dr. Huang serves/served on the editorial board for IEEE Transactions on Communications (TCOM), ACM Transactions on Modeling and Performance Evaluation of Computing Systems (ToMPECS), IEEE Journal on Selected Areas in Communications (JSAC-guest editor) and IEEE/ACM Transactions on Networking (ToN). He is a senior member of IEEE and a member of ACM. Dr. Huang received the Outstanding Teaching Award from Tsinghua university in 2014. He received the Google Research Award and the Microsoft Research Asia Collaborative Research Award in 2014, and was selected into the MSRA StarTrack Program in 2015. Dr. Huang won the ACM SIGMETRICS Rising Star Research Award in 2018.

Session Chair

Jia Liu (OSU), Luoyi Fu (SJTU)

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