Papers Stochastic Block Model
“Stochastic Block Model” 태그가 달린 논문 370편 · 필터 해제
Effective Stimulus Propagation in Neural Circuits: Driver Node Selection
Precise control of signal propagation in modular neural networks represents a fundamental challenge in computational neuroscience. We establish a framework for identifying optimal control nodes that maximize stimulus tra…
Stochastic Block ModelSpectral clustering for dependent community Hawkes process models of temporal networks
Temporal networks observed continuously over time through timestamped relational events data are commonly encountered in application settings including online social media communications, financial transactions, and inte…
parameter estimationStochastic Block ModelUnsupervised Learning for Optimal Transport plan prediction between unbalanced graphs
Optimal transport between graphs, based on Gromov-Wasserstein and other extensions, is a powerful tool for comparing and aligning graph structures. However, solving the associated non-convex optimization problems is comp…
Stochastic Block ModelPartition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening
Filtering-based graph neural networks (GNNs) constitute a distinct class of GNNs that employ graph filters to handle graph-structured data, achieving notable success in various graph-related tasks. Conventional methods a…
Anomaly DetectionGraph Anomaly DetectionNode ClassificationStochastic Block ModelCommunity and hyperedge inference in multiple hypergraphs
Hypergraphs, capable of representing high-order interactions via hyperedges, have become a powerful tool for modeling real-world biological and social systems. Inherent relationships within these real-world systems, such…
Community DetectionHyperedge PredictionStochastic Block ModelConsensus on Open Multi-Agent Systems Over Graphs Sampled from Graphons
We show how graphons can be used to model and analyze open multi-agent systems, which are multi-agent systems subject to arrivals and departures, in the specific case of linear consensus. First, we analyze the case of re…
Stochastic Block ModelCommunities in the Kuramoto Model: Dynamics and Detection via Path Signatures
The behavior of multivariate dynamical processes is often governed by underlying structural connections that relate the components of the system. For example, brain activity which is often measured via time series is det…
Community DetectionStochastic Block ModelTime SeriesMulti-Agent Q-Learning Dynamics in Random Networks: Convergence due to Exploration and Sparsity
Beyond specific settings, many multi-agent learning algorithms fail to converge to an equilibrium solution, and instead display complex, non-stationary behaviours such as recurrent or chaotic orbits. In fact, recent lite…
Q-LearningStochastic Block ModelStatistical physics analysis of graph neural networks: Approaching optimality in the contextual stochastic block model
Graph neural networks (GNNs) are designed to process data associated with graphs. They are finding an increasing range of applications; however, as with other modern machine learning techniques, their theoretical underst…
Node ClassificationStochastic Block ModelLow degree conjecture implies sharp computational thresholds in stochastic block model
We investigate implications of the (extended) low-degree conjecture (recently formalized in [MW23]) in the context of the symmetric stochastic block model. Assuming the conjecture holds, we establish that no polynomial-t…
Stochastic Block ModelAlgorithmic contiguity from low-degree conjecture and applications in correlated random graphs
In this paper, assuming a natural strengthening of the low-degree conjecture, we provide evidence of computational hardness for two problems: (1) the (partial) matching recovery problem in the sparse correlated Erd\H{o}s…
Stochastic Block ModelHeterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models
We study a novel heterogeneous multi-agent multi-armed bandit problem with a cluster structure induced by stochastic block models, influencing not only graph topology, but also reward heterogeneity. Specifically, agents …
Multi-Armed BanditsStochastic Block ModelCommunity Detection for Contextual-LSBM: Theoretical Limitations of Misclassification Rate and Efficient Algorithms
The integration of network information and node attribute information has recently gained significant attention in the community detection literature. In this work, we consider community detection in the Contextual Label…
AttributeCommunity DetectionStochastic Block ModelExact Matching in Correlated Networks with Node Attributes for Improved Community Recovery
We study community detection in multiple networks whose nodes and edges are jointly correlated. This setting arises naturally in applications such as social platforms, where a shared set of users may exhibit both correla…
AttributeCommunity DetectionGraph MatchingStochastic Block ModelLow coordinate degree algorithms II: Categorical signals and generalized stochastic block models
We study when low coordinate degree functions (LCDF) -- linear combinations of functions depending on small subsets of entries of a vector -- can test for the presence of categorical structure, including community struct…
Stochastic Block ModelMatrix Concentration for Random Signed Graphs and Community Recovery in the Signed Stochastic Block Model
We consider graphs where edges and their signs are added independently at random from among all pairs of nodes. We establish strong concentration inequalities for adjacency and Laplacian matrices obtained from this famil…
Community DetectionStochastic Block ModelOptimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks
We delve into the challenge of semi-supervised node classification on the Contextual Stochastic Block Model (CSBM) dataset. Here, nodes from the two-cluster Stochastic Block Model (SBM) are coupled with feature vectors, …
Node ClassificationregressionStochastic Block ModelTransductive LearningOn the Robustness of Spectral Algorithms for Semirandom Stochastic Block Models
In a graph bisection problem, we are given a graph $G$ with two equally-sized unlabeled communities, and the goal is to recover the vertices in these communities. A popular heuristic, known as spectral clustering, is to …
Stochastic Block ModelMultiplex Dirichlet stochastic block model for clustering multidimensional compositional networks
Network data often represent multiple types of relations, which can also denote exchanged quantities, and are typically encompassed in a weighted multiplex. Such data frequently exhibit clustering structures, however, tr…
ClusteringStochastic Block ModelCommunity Detection with Heterogeneous Block Covariance Model
Community detection is the task of clustering objects based on their pairwise relationships. Most of the model-based community detection methods, such as the stochastic block model and its variants, are designed for netw…
Community DetectionmodelStochastic Block Model