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Biological networks are dynamic structures. They continuously evolve by rewiring their interactions. These rewirings happen at different rates for different cells, and the rates can change over time, yet we can only observe the cell at a limited number ...

Online social networks are crucial in propagating information and exerting influence through word-of-mouth transmission. Influence maximization (IM) is the fundamental task in social network analysis to find the group of nodes that maximizes the influence ...

Influence maximization (IM) is the fundamental study of social network analysis. The IM problem finds the top k nodes that have maximum influence in the network. Most of the studies in IM focus on maximizing the number of activated nodes in the static ...

Network embedding (NE) tries to learn the potential properties of complex networks represented in a low‐dimensional feature space. However, the existing deep learning‐based NE methods are time‐consuming as they need to train a dense architecture ...

This survey article presents a comprehensive and conceptual overview of anomaly detection (AD) using dynamic graphs. We focus on existing graph-based AD techniques and their applications to dynamic networks. The contributions of this survey article ...

Many instances of coordination problems in social networks can be characterized by the well-known graph coloring game—a task in which agents are incentivized to choose an attribute, represented by a color, to be different from linked neighbors. ...

Distributed computing has been concerned with the topics of faults and failures from its very beginning (before PODC was born). The consensus problem was at the forefront of the research efforts, and an extensive body of literature on the subject was ...

The best known solutions for k-message broadcast in dynamic networks of size n require Ω (nk) rounds. In this article, we see if these bounds can be improved by smoothed analysis. To do so, we study perhaps the most natural randomized algorithm for ...

Deep learning is commonly used to make personalized recommendations to users for a wide variety of activities. However, deep learning recommendation model (DLRM) training is increasingly dominated by all-to-all and many-to-many communication patterns. ...

Dynamic link prediction has become a trending research subject because of its wide applications in the web, sociology, transportation, and bioinformatics. Currently, the prevailing approach for dynamic link prediction is based on graph neural networks, in ...

The growing popularity of online social networks is quite evident nowadays and provides an opportunity to allow researchers in finding solutions for various practical applications. Link prediction is the technique of understanding network structure and ...

Environmental, social and governance (ESG) considerations play an increasingly important role due to the growing focus on sustainability globally. Entities, such as banks and investors, utilize ESG ratings of companies issued by specialized rating ...

We study a novel problem of continuously predicting a number of user-subscribed continuous analytics targets (CATs) in dynamic networks. Our architecture includes any dynamic graph neural network model as the back end applied over the network data, and ...

Most real-world networks evolve over time. Existing literature proposes models for dynamic networks that are either unlabeled or assumed to have a single membership structure. On the other hand, a new family of Mixed Membership Stochastic Block Models (...

The emergence of cooperation is a major question in game theory and one under-studied aspect is the effects of networks on the emergent behaviour. My PhD asks this question over multiple collaborations and projects, using methodologies from (evolutionary)...

The tendency for individuals to form social ties with others who are similar to themselves, known as homophily, is one of the most robust sociological principles. Since this phenomenon can lead to patterns of interactions that segregate people along ...

Honeypots are technologies aimed at thwarting adversaries by instituting attractive services that are inconsequential to the legitimate objectives of a network. Low-interaction honeypots are lightweight, but provide a limited representation of real ...

Dynamic evolving networks capture temporal relations in domains such as social networks, communication networks, and financial transaction networks. In such networks, temporal motifs, which are repeated sequences of time-stamped edges/transactions, ...

Temporal graph embedding has been widely studied thanks to its superiority in tasks such as prediction and recommendation. Despite the advances in algorithms and novel frameworks such as deep learning, there has been relatively little work on ...

With the recent advances in human sensing, the push to integrate human mobility tracking with epidemic modeling highlights the lack of groundwork at the mesoscale (e.g., city-level) for both contact tracing and transmission dynamics. Although GPS data ...