Distance-based community search
Speaker: Francesco Bonchi
Abstract: Suppose we have identified a set of subjects in a terrorist network suspected of organizing an attack. Which other subjects, likely to be involved, should we keep under control? Similarly, given a set of patients infected with a viral disease, which other people should we monitor? Given a set of proteins of interest, which other proteins participate in pathways with them? Each of these questions can be modeled as a graph-query problem: given a graph G = (V,E) and a set of query vertices Q, find a subgraph H of G which “explains” the connections existing among the nodes in Q, hat is to say that H must be connected and contain all query vertices in Q.
We start by providing a brief survey of various measures and methods defined for this network problem, then we turn our attention to the problem of finding a “minimum Wiener connector”, i.e., the subgraph of G that connects all query vertices and that minimizes the sum of all pairwise shortest-path distances between its vertices (Wiener Index). We show that the minimum Wiener connector is smaller and denser than other methods in the literature, and it contains highly central nodes.
In the second part of the talk, we relax the constraint of connecting all the query vertices. Relaxing the connectedness requirement allows the connector to detect multiple communities and to be tolerant to outliers. We achieve this by introducing the new measure of network inefficiency and by instantiating our search for a selective connector as the problem of finding the minimum inefficiency subgraph. We show that our problem is hard and devise efficient algorithms to approximate it. By means of several case studies in a variety of application domains (such as human brain, cancer, and food networks), we show that our minimum inefficiency subgraph produces high-quality solutions, exhibiting all the desired behaviors of a selective connector.
Finally, we extend the present notions to the case of temporal dynamic networks showing how our tools can be used to track a community of interest adaptively in time.
Bio: Francesco Bonchi is Deputy Director at the ISI Foundation, Turin, Italy, with responsibility over the Industrial Research area. At ISI Foundation, he is also Research Leader for the “Algorithmic Data Analytics” group. He is also (part-time) Research Director for Big Data & Data Science at Eurecat (Technological Center of Catalunya), Barcelona. Before he was Director of Research at Yahoo Labs in Barcelona, Spain, where he was leading the Web Mining Research group. His recent research interests include mining query-logs, social networks, and social media, as well as the privacy issues related to mining these kinds of sensible data. In the past he has been interested in data mining query languages, constrained pattern mining, mining spatiotemporal and mobility data, and privacy preserving data mining. He has more than 200 publications in these areas. He also filed 15 US patents, and got granted 8 US patents. He is co-editor of the book “Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques”. More info at http://www.francescobonchi.com/
Computational analysis of interactomes: Challenges, solutions, and opportunities
Speaker: Tamer Kahveci, Ph.D
Abstract: Biological networks of an organism show how different bio-chemical entities, such as enzymes or genes interact with each other to perform vital functions for that organism. Dr. Kahveci’s lab is focusing on developing computational methods that will help in understanding the functions of large-scale biological networks. In this talk, we will discuss some of the recent research activities at Dr. Kahveci’s lab. More specifically, we will focus on how different network models, such as static, probabilistic, dynamic, and multilayer models, address various challenges in computational biology. We will first consider challenges centered on uncertainty in the topology of biological networks. We will discuss our new mathematical model, which represent probabilistic networks as collections of polynomials. We show that this is a powerful model that enables solving seemingly very tough computational problems on probabilistic networks efficiently and precisely. We will then discuss how the dynamic behavior of the network affects how we can approach to some of the fundamental computational problems on biological network analysis such as motif counting.
Bio: Tamer Kahveci received his Ph.D. degree in Computer Science from University of California at Santa Barbara in 2004. He is currently a Professor and Associate Chair of Academic Affairs in the Computer and Information Science and Engineering Department at the University of Florida, serving as the Associate Chair of Academic affairs. Dr. Kahveci received the Ralph E. Powe Junior Faculty Enhancement award in 2006, CSB best paper award in 2008, the NSF Career award in 2009, the ACM-BCB (Bioinformatics and Computational Biology) best student paper award in 2010, ACM-BCB honorary best paper award in 2011, and BiCoB best paper award in 2018. His research focuses on bioinformatics. He has worked on indexing sequence and protein structure databases, sequence alignment and computational analysis of biological networks.
Dr. Kahveci has served as the PC co-chair of the ACM BCB conference in 2012 and 2017, the BioKDD workshop and the International Workshop on Robustness and Stability of Biological Systems and Computational Solutions in 2012, the Workshop on Epigenomics and Cell Function in 2013, and the Workshop on Computational Network Analysis, the Workshops Chair of the ACM-BCB conference in 2014, 2015, 2016, 2017, 2018, and 2019. He served as the Tutorials Chair of the ACM BCB and the IEEE BIBM conferences in 2015, and Workshop Chair in 2016. He is a member of the governing board of the ACM SIGBIO and the chair of the steering committee member of the ACM-BCB. He is a member of the editorial review board for of the journal International Journal of Knowledge Discovery in Bioinformatics (IJKDB). He was the lead guest editor of the Journal of Advances in Bioinformatics, special issue on “Computational analysis of biological networks” and associate editor in IEEE/ACM Transactions on Computational Biology and Bioinformatics. In addition to these, he has served on the program committees of numerous computational biology and database conferences.
Building Information Modeling as Digital Twin for Sustainable Design Optimization
Speaker: Tien Hung-Le, Ph.D
Abstract: The demand of a fully equipped information model or a better media for communication in construction in order to keep all project stakeholders to be well informed is always received great concerns. The progress in ICT recently leads to the advance in numerous applications in construction and Building Information Modelling (BIM), would be the remarkable result of ICT progress. The purpose to achieve sustainable design and meet the climate change now can be reached with the help of advances in computer software in particular and ICT in general. The quality of Multidiscipline Building Information Model (BIM Model) including computable architectural model, structural model, MEP model, cost estimation and facility management oriented model is crucial for the productivity, performance of all related disciplines. This article illustrates the Computable BIM Model for Architecture, Structure, Mechanical – Electrical – Plumbing (MEP) to help architects, designers doing sustainable design such as acoustic comfort, lighting design on effective cost manner with many rapid design options from the conceptual design phase, to facilitate wind load simulation, predict structural behavior under fierce conditions, to estimate heat load, HVAC system performance for energy saving and sustainable design. The open source based BIM applications for computational design optimization that allows the designers to achieve the optimum design with the help of ICT tools. Furthermore, BIM implementation is 90% sociological implication while technology plays only 10%…The application of computational data and social network analysis project management, group dynamics…helps the project owners have insight views on BIM Team activities and predicts the on going trends.
Bio: Dr.Le Hung Tien graduated from Faculty of Mechanical Engineering, Hochiminh City University (Bach Khoa Universiiy – BKU) with excellent ranking. He received Ph.D Degree in Materials Science of Hanoi University of Science and Technology (HUST) and Technical University of Berlin. Dr.Tien is Autodesk certified Instructor (2000), Computer Graphics and BIM Instructors on Hochiminh Television Station (HTV) from 2006-2010. Dr.Tien is the Dean of Faculty of Engineering, Van Lang University (VLU). He was the principal researchers of 15 projects with 2 state projects and 11 municipal projects. Dr.Tien is Director of CMU project (Carnegie Mellon University Project), the collaboration project between VLU and CMU in software engineering and data science.
On Differentially Private Graph Sparsification and Applications
Speaker: Raman Arora, Ph.D
Abstract: Data from social and communication networks have become a rich source to gain useful insights into the social, behavioral, and information sciences. Such data are naturally modeled as observations on a graph and encodes rich, fine-grained, and structured information. At the same time, due to the seamless nature of data acquisition, often collected through personal devices, the information contained in network data is often highly sensitive, which raises valid privacy concerns pertaining the analysis and release of such data. We address these issues by presenting a novel algorithm that can be used to publish a succinct differentially private representation of network data with minimal degradation in accuracy for various graph-related tasks.
Bio: Raman Arora is a professor of computer science at the Johns Hopkins University. His research interests are in machine learning, representation learning, stochastic optimization, and differential privacy.
TCyber Security Threat Intelligence: Challenges and Research Opportunities
Speaker: Kim-Kwang Raymond Choo, Ph.D
Abstract: Cyber threat intelligence and analytic is among one of the fastest growing interdisciplinary fields of research bringing together researchers from different fields such as digital forensics, political and security studies, criminology, cyber security, big data analytics, machine learning, etc. to detect, contain and mitigate advanced persistent threats and fight against organized cybercrimes. In this presentation, we will discuss some of the challenges underpinning this inter- / trans- /multi-disciplinary field as well as research opportunities (e.g. how can we leverage advances in deep learning to better predict cyber attacks?).
Bio: Kim-Kwang Raymond Choo received the Ph.D. in Information Security in 2006 from Queensland University of Technology, Australia. He currently holds the Cloud Technology Endowed Professorship at The University of Texas at San Antonio (UTSA), and has a courtesy appointment at the University of South Australia. In 2015 he and his team won the Digital Forensics Research Challenge organized by Germany’s University of Erlangen-Nuremberg. He is also the recipient of the 2019 IEEE Technical Committee on Scalable Computing (TCSC) Award for Excellence in Scalable Computing (Middle Career Researcher), 2018 UTSA College of Business Col. Jean Piccione and Lt. Col. Philip Piccione Endowed Research Award for Tenured Faculty, Outstanding Associate Editor of 2018 for IEEE Access, British Computer Society’s 2019 Wilkes Award Runner-up, 2019 JWCN Best Paper Award, Korea Information Processing Society’s JIPS Survey Paper Award (Gold) 2019, IEEE Blockchain 2019 Outstanding Paper Award, IEEE TrustCom 2018 Best Paper Award, ESORICS 2015 Best Research Paper Award, 2014 Highly Commended Award by the Australia New Zealand Policing Advisory Agency, Fulbright Scholarship in 2009, 2008 Australia Day Achievement Medallion, and British Computer Society’s Wilkes Award in 2008. He is also a Fellow of the Australian Computer Society, an IEEE Senior Member, and Co-Chair of IEEE Multimedia Communications Technical Committee’s Digital Rights Management for Multimedia Interest Group.
A Privacy-preserving Meta-data Analytics Platform for Detecting Cyber Abuse – Design, Deployment, Results and Insights for Future
Speaker: Sriram Chellappan, Ph.D
Abstract: we present perspectives in creating a massive scale digital platform that enables youth to share content that suits their own privacy expectations, while still contributing to cyber-abuse research.
Bio: Sriram Chellappan is an Associate Professor in The Department of Computer Science and Engineering at University of South Florida, where he directs the SCoRe (Social Computing Research) Lab. His primary interests lie in many aspects of how Society and Technology interact with each other, particularly within the realms of Cyber Safety, Smart Health and Privacy. He is also interested in Mobile and Wireless Networking, Cyber-Physical Systems, Distributed and Cloud Computing. Sriram’s research is supported by grants from National Science Foundation, Department of Education, Army Research Office, National Security Agency, DARPA and Missouri Research Board. Prior to this appointment, he was an Associate Professor in the Computer Science Dept. at Missouri University of Science and Technology . Sriram received the PhD degree in Computer Science and Engineering from The Ohio-State University in 2007. Sriram received the NSF CAREER Award in 2013. He also received the Missouri S&T Faculty Excellence Award in 2014, the Missouri S&T Outstanding Teaching Commendation Award in 2014, and the Missouri S&T Faculty Research Award in 2015.
On human mobility and sociality through the lens of mobile phone data
Speaker: Sabrina Gaito, Ph.D
Abstract: Mobile phones are desired options for tracking and mining user behaviour in daily life and can be used to collect data about people’s behaviour in multiple aspects. These include people’s mobility, i.e. their places, how they move among places and whom they could meet while spending time in those places. In this talk we present our ongoing line of work on human mobility and sociality in urban spaces.
Bio: Sabrina Gaito is a professor in the department of Computer Science in University of Milan, where she teaches social network analysis and machine learning. Her research activity takes place within the data and network science, with a focus on social networking, human mobility and behaving. She is in the editorial board of leading journals such as PlosOne and the Applied Network Science Journal and co-organizer of conferences and workshops in the fields of complex networks.
Discussing online. When does polarization become a barrier to digital political communication?
Speaker: José Manuel Robles, Ph.D
Abstract: In this presentation we consider that polarisation does not entail, per se, a risk for the proper development of political communication. For instance, in electoral contexts, especially if they are bipartisan, polarisation is an expected result. Our main theoretical hypothesis is that polarisation becomes an important barrier to rational and balanced debate when the views of the participants are extreme and/ or associated with negative affective mind-sets towards a political opponent. To test this hypothesis, we carried out analysis to measure political polarisation, as well as affective mindsets (love/hate) in the debate on Twitter during the United States General Election campaign. Our results show that, in general terms, there is not a very high polarization during the debate under study. However, the most active accounts, presumably accounts associated with the media or organizations with a political interest, present a negative affective disposition making the debate extreme.
Bio: José Manuel is Assistant Professor of sociology at Complutense University of Madrid (Spain). His research interests are Social Networks, Political Communication and Digital Political Participation.