More Like this

1. The Block Point Process Model for Continuous-time Event-based Dynamic Networks

   https://doi.org/10.1145/3308558.3313633  

   Junuthula, Ruthwik; Haghdan, Maysam; Xu, Kevin S.; Devabhaktuni, Vijay (January 2019, Proceedings of the World Wide Web Conference)

   We consider the problem of analyzing timestamped relational events between a set of entities, such as messages between users of an on-line social network. Such data are often analyzed using static or discrete-time network models, which discard a significant amount of information by aggregating events over time to form network snapshots. In this paper, we introduce a block point process model (BPPM) for continuous-time event-based dynamic networks. The BPPM is inspired by the well-known stochastic block model (SBM) for static networks. We show that networks generated by the BPPM follow an SBM in the limit of a growing number of nodes. We use this property to develop principled and efficient local search and variational inference procedures initialized by regularized spectral clustering. We fit BPPMs with exponential Hawkes processes to analyze several real network data sets, including a Facebook wall post network with over 3,500 nodes and 130,000 events. 

   more » « less
2. CHIP: A Hawkes process model for continuous-time networks with scalable and consistent estimation

   Arastuie, Makan; Paul, Subhadeep; Xu, Kevin S. (December 2020, Advances in neural information processing systems)

   null (Ed.)

   In many application settings involving networks, such as messages between users of an on-line social network or transactions between traders in financial markets, the observed data consist of timestamped relational events, which form a continuous-time network. We propose the Community Hawkes Independent Pairs (CHIP) generative model for such networks. We show that applying spectral clustering to an aggregated adjacency matrix constructed from the CHIP model provides consistent community detection for a growing number of nodes and time duration. We also develop consistent and computationally efficient estimators for the model parameters. We demonstrate that our proposed CHIP model and estimation procedure scales to large networks with tens of thousands of nodes and provides superior fits than existing continuous-time network models on several real networks. 

   more » « less
3. A mutually exciting latent space Hawkes process model for continuous-time networks

   Huang, Zhipeng; Soliman, Hadeel; Paul, Subhadeep; Xu, Kevin S. (January 2022, Proceedings of Machine Learning Research)

   Networks and temporal point processes serve as fundamental building blocks for modeling complex dynamic relational data in various domains. We propose the latent space Hawkes (LSH) model, a novel generative model for continuous-time networks of relational events, using a latent space representation for nodes. We model relational events between nodes using mutually exciting Hawkes processes with baseline intensities dependent upon the distances between the nodes in the latent space and sender and receiver specific effects. We demonstrate that our proposed LSH model can replicate many features observed in real temporal networks including reciprocity and transitivity, while also achieving superior prediction accuracy and providing more interpretable fits than existing models. 

   more » « less
4. EC-SBM synthetic network generator

   https://doi.org/10.1007/s41109-025-00701-2  

   Vu-Le, The-Anh; Anne, Lahari; Chacko, George; Warnow, Tandy (December 2025, Applied Network Science)

   Abstract Generating high-quality synthetic networks with realistic community structure is vital to effectively evaluate community detection algorithms. In this study, we propose a new synthetic network generator called the Edge-Connected Stochastic Block Model (EC-SBM). The goal of EC-SBM is to take a given clustered real-world network and produce a synthetic network that resembles the clustered real-world network with respect to both network and community-specific criteria. In particular, we focus on simulating the internal edge connectivity of the clusters in the reference clustered network. Our performance study on large real-world networks shows that EC-SBM is generally more accurate with respect to network and community criteria than currently used approaches for this problem. Furthermore, we demonstrate that EC-SBM can complete analyses on several real-world networks with millions of nodes. 

   more » « less
5. Stochastic blockmodeling for learning the structure of optimization problems

   https://doi.org/10.1002/aic.17415  

   Mitrai, Ilias; Tang, Wentao; Daoutidis, Prodromos (September 2021, AIChE Journal)

   Abstract Decomposition‐based solution algorithms for optimization problems depend on the underlying latent block structure of the problem. Methods for detecting this structure are currently lacking. In this article, we propose stochastic blockmodeling (SBM) as a systematic framework for learning the underlying block structure in generic optimization problems. SBM is a generative graph model in which nodes belong to some blocks and the interconnections among the nodes are stochastically dependent on their block affiliations. Hence, through parametric statistical inference, the interconnection patterns underlying optimization problems can be estimated. For benchmark optimization problems, we show that SBM can reveal the underlying block structure and that the estimated blocks can be used as the basis for decomposition‐based solution algorithms which can reach an optimum or bound estimates in reduced computational time. Finally, we present a general software platform for automated block structure detection and decomposition‐based solution following distributed and hierarchical optimization approaches. 

   more » « less