|17:20 - 17:40||Alexandru Topirceanu and Mihai Udrescu||Topological Fragility versus Antifragility: Understanding the Impact of Real-time Repairs in Networks Under Targeted Attacks|
|17:40 - 18:00||Qasim Pasta and Faraz Zaidi||Model to Generate Benchmark Graphs Based on Evolution Dynamics||Video|
|18:05 - 18:20||Vladimir Marbukh||Network Formation by Contagion Averse Agents: Modeling Bounded Rationality with Logit Learning|
|18:20 - 18:40||Sam De Winter, Tim Decuypere, Sandra Mitrovic, Bart Baesens and Jochen De Weerdt||Combining Temporal Aspects of Dynamic Networks with Node2Vec for a more Efficient Dynamic Link Prediction|
Network dynamics is one of the biggest challenge that emerged in recent years in network science. Real life networks cannot be considered anymore as static entities that we can pin to the wall and measure once and for all. They are, on the contrary, subject to several dynamics processes:
|Dynamics OF Networks||Dynamics ON Networks|
|Dynamic Social Network Analysis||Dynamical processes on networks|
|Dynamic network embedding||Diffusion of information and innovations|
|Network event detection||Epidemic models on graphs|
|Time-aware link prediction||Higher order networks|
|Evolutionary community / cluster discovery||Markovian dynamics on networks|
|Dynamic network visualization||Signal processing on graphs|
|Dynamic network generative models||Random walk processes|
Network science, network analysis, and network mining are new scientific topics that emerged in recent years and are quickly growing. Instead of studying the properties of entities, network science focuses on the interaction between these entities. The tremendous quantity of relational data that has become available (Online Social Networks, cell phones, the Internet and the Web, trip datasets, etc.) encourage new research on the topic.
In the last years we have witnessed a shift from static network analysis to a dynamic network analysis, i.e., the study of networks whose structure change over time. As time goes by, all the perturbations which occur to the network topology due to the rise and fall of nodes and edges have repercussions on the network phenomena we are used to observe. As an example, evolution over time of social interactions in a network can play an important role in the diffusion of an infectious disease.
Nowadays, one of the fascinating challenges is to analyze the structural dynamics of real world networks and how they impact on the processes which occur on them, i.e., the spreading of social influence and diffusion of innovations. Results in this field will enable a better understanding of important aspects of human behavior as well as a more detailed characterization of the complex interconnected society we live in. Since the last decades, diffusive and spreading phenomena were facilitated by the enormous popularity of the Internet and the evolution of social media that enabled an unprecedented exchange of information. For this reason, understanding how social relationships unravel in these rapidly evolving contexts represents an important fields of research.
The purpose of this workshop is to encourage principled research that will lead to the advancement of the social science in time-evolving networks. The workshop will seek top-quality submissions addressing important topics such as: dynamic network modeling, time-aware network mining approaches, social influence spreading, diffusion processes in dynamic networks and forecast of network topology perturbation.