If you're not familiar with the research field, let me share a little secret. Writing research papers can be a lengthy and challenging process, often taking several months from submission to publication. Here, you can find some of my published papers. For the full list, I recommend visiting my Google Scholar page.
In this paper, we (the authors) introduce Chronnet, an approach to construct networks from spatio-temporal datasets. The method distributes the data points within a specific area, then divides the entire region into grid cells. By representing these grid cells as nodes and connecting them chronologically, it efficiently identifies recurrent events between cells. This approach effectively captures intricate data patterns, making it a robust tool for processing large spatiotemporal datasets.
This work introduces an index for measuring engagement in group conversations while prioritizing privacy, a crucial concern today. The method doesn't involve reading message content but focuses solely on metadata, such as sender and timestamp. To construct the network, each sender is represented as a node, linked by a sequence of messages within a time window. Subsequently, we apply the proposed formula to calculate the Engagement Index (EI) and identify the most engaged members.
This study examines the behavior of 'infected' links within a larger network by employing a random variable to determine disease propagation. The analysis explores the relaxation timescale of this variable under annealed and quenched limits, involving two types of underlying networks: scale-free and exponential degree distribution. The findings highlight the critical influence of the relaxation timescale on the subgraph's topology and, consequently, the efficiency of disease/information transmission throughout the network.