Federated learning optimizes power for reliable V2V communication.
problem Minimizing power consumption for reliable V2V communication.
method Decentralized federated learning for estimating extreme queue lengths.
result Significant reduction in extreme events of queue lengths.
Deep learning optimizes vehicular communication zones for efficient data dissemination.
problem Overdimensioning and inefficient communication in vehicular floating content.
method Deep learning is used to select optimal broadcasting areas (Anchor Zones) for efficient message dissemination.
result The proposed method achieves an accuracy of 89.7% in predicting optimal Anchor Zones, saving up to 27% of resources.
Develops a neural network for precise vehicle trajectory prediction.
problem Improving situational awareness in vehicular networks for safety applications.
method Two-layer neural network predicting vehicle parameters and trajectory points.
result Significantly improved prediction accuracy compared to existing methods.
Proposes a privacy-preserving system for federated learning of road networks.
problem Privacy and security of data shared between vehicles and infrastructure.
method Federated learning over V2V and V2N links, non-IID dataset modeling.
result Improves learning performance and prevents eavesdropping.
The paper analyzes vehicle encounters using driving primitives.
problem Understanding complex vehicle encounters for autonomous driving.
method Decompose driving data into primitives using nonparametric Bayesian learning.
result More than 4000 driving primitives identified from 976 encounters.
HICODE detects hidden communities in social networks.
problem Weak, natural communities hidden within strong, dominant communities.
method HICODE (HIdden COmmunity DEtection) that identifies both hidden and dominant communities.
result HICODE outperforms state-of-the-art methods in uncovering both hidden and dominant communities.
We introduce community trees to summarize network structures.
problem Stability of community structures in networks.
method Clique percolation method (CPM) and persistent diagrams.
result Total star number (TSN) provides an upper bound on community tree changes.
A new framework maximizes influence spread in social networks by accounting for inter-community diffusion.
problem Real-world social networks have inter-community influence that is often overlooked in community-based IM approaches.
method Community-IM++ uses a heuristic based on community-based diffusion degree and progressive budgeting to model and prioritize cross-community diffusion.
result Community-IM++ achieves near-greedy influence spread at up to 100 times lower runtime than existing methods.
Community detection improves stock market portfolio optimization.
problem Improving portfolio optimization in financial markets.
method Community detection in correlation-based networks of worldwide stock markets.
result Portfolios constructed using community detection outperform traditional methods.
Study exact community detection in k-community Gaussian mixtures with different intensities.
problem Community detection in k-community Gaussian mixtures with varying intensities.
method Explicitly find the threshold for exact recovery of maximum likelihood estimation.
result Threshold for exact recovery of maximum likelihood estimation is identified.
The paper introduces a method for detecting principal communities and embedding vertices.
problem Detecting and embedding vertices in graphs with community structure.
method Principal graph encoder embedding method that detects principal communities and produces vertex embeddings.
result The method successfully detects principal communities and produces accurate vertex embeddings.
Method detects shared and private communities in multilayer networks.
problem Detecting shared and private communities in multilayer networks.
method Variational Bayes approach for jointly inferring shared and unshared hidden communities.
result Our method outperforms state-of-the-art algorithms in detecting communities.
Improved distributed learning with reduced communication costs.
problem Efficient communication in resource-constrained environments for distributed learning.
method Proposed a cost-effective partial communication protocol.
result Communication cost is reduced to O(logT), improving significantly on full communication. vGraph learns community membership and node representation jointly.
problem Independent study of community detection and node representation learning limits graph analysis.
method vGraph is a probabilistic generative model that learns community membership and node representation collaboratively.
result vGraph outperforms many baselines in both community detection and node representation learning.
Statistical estimates can often be improved by fusion of data from several different sources. One example is so-called ensemble methods which have been successfully applied in areas such as machine learning for classification and clustering. In this paper, we present an ensemble method to improve community detection by…
Study compares community detection methods in various networks.
problem Determine which community detection method is best for specific network types.
method Comprehensive empirical analysis of multiple methods on diverse network categories.
result Identifies different types of communities produced by various methods.
New GNN model detects overlapping communities better than existing methods.
problem Detecting overlapping communities in graphs.
method Graph Neural Network (GNN) for overlapping community detection.
result The proposed GNN model outperforms existing baselines significantly.
MACC learns communication protocols by adapting counterfactual reasoning.
problem Credit assignment and non-stationarity in communication environments.
method Adapts counterfactual reasoning to overcome credit assignment and uses action policy and Q-function of other agents to handle non-stationarity.
result MACC outperforms state-of-the-art baselines in four scenarios.
TMSCD detects multi-scale communities in temporal networks automatically.
problem Discovering multi-scale communities in large, evolving networks.
method Spectral multilayer formulation of MM method with automatic parameter selection.
result Automatic detection of multi-scale communities without manual parameter selection.
A novel framework for adaptive multi-agent communication in reinforcement learning.
problem Manual specification of communication structures in multi-agent reinforcement learning.
method Learning Structured Communication (LSC) framework using hierarchical graph neural networks.
result Adaptive hierarchical formations and efficient message propagation among agents.
Survey on community detection methods and their theoretical properties.
problem Consistent estimation of community labels in networks.
method Various community detection methods and their theoretical properties.
result Review of community detection methods and their theoretical properties.
Paper studies community detection in Degree-Corrected Block Models.
problem Community detection in networks.
method Derives asymptotic minimax risks and proposes an algorithm for consistent community detection.
result Shows how minimax risks depend on degree-correction parameters and network connectivities.
A framework for multi-agent communication over noisy channels in reinforcement learning.
problem Effective communication between multiple agents in a noisy environment.
method A novel multi-agent partially observable Markov decision process (MA-POMDP) framework considering noisy communication channels.
result Jointly learned policies outperform separate learning of communication and decision making.
Generative model improves local community detection in networks.
problem Finding a single community in a large network using only a small part of it.
method Starting from a generative model for networks with community structure, approximating the unobserved parts to detect local communities.
result The proposed methods show comparable or improved results compared to state-of-the-art local community detection algorithms.
A new method reduces communication in distributed learning by skipping less informative gradient updates.
problem Efficient communication in distributed machine learning.
method Quantizes and skips less informative gradients to reduce communication overhead.
result Proves linear convergence rate similar to gradient descent with significant communication savings.
New metrics needed for better understanding emergent communication in multi-agent systems.
problem Current metrics for emergent communication are insufficient for complex environments.
method Training deep reinforcement learning agents in simple games to analyze communication effectiveness.
result Messages in emergent communication can be misleading and do not always impact the environment.
GRADE models evolving graph dynamics by learning node and community representations.
problem Lack of tools to study temporal community dynamics in evolving graphs.
method GRADE is a probabilistic model that learns evolving node and community representations via a random walk prior and variational inference.
result GRADE outperforms baselines in dynamic link prediction and dynamic community detection.
Proposes a new framework for detecting overlapping and non-overlapping communities.
problem Lack of methods for both overlapping and non-overlapping community detection.
method Integrated framework based on primary node criteria of internal and external association degrees.
result Outperforms existing methods on evaluation criteria.
Paper studies fundamental limits of communication in distributed learning.
problem Communication efficiency in model aggregation for distributed learning.
method Rate-Distortion approach to model aggregation as a vector Gaussian CEO problem.
result Derives rate region bound and sum-rate-distortion function for model aggregation.
New algorithm reduces communication traffic in decentralized learning.
problem Communication bottleneck in decentralized learning for low-bandwidth workers.
method Sparsification and adaptive peer selection to reduce communication traffic.
result Significant reduction in communication traffic compared to existing methods.
Study recovers community structure from coarse graph measurements.
problem Community recovery from low-resolution graph measurements.
method Formalized coarsening process of graph measurements, developed conditions for perfect recovery.
result Simple and closed-form asymptotic conditions for perfect recovery of coarse graph communities.
Paper reduces communication in distributed machine learning.
problem Reduces burdensome communication in distributed machine learning.
method Introduces communication-censoring technique to reduce transmissions of variables.
result CSGD algorithm achieves same convergence rate as SGD but with significant communication reduction.
New method detects overlapping communities in weighted graphs without pure nodes assumption.
problem Detect overlapping communities in weighted graphs without making pure nodes assumption.
method Convex optimization-based approach for weighted graphs.
result Success on artificial and real-world datasets.
Enhances Infomap algorithm to prioritize metadata in community detection.
problem Balancing metadata importance in community detection.
method Introduces a tuning parameter to the Infomap algorithm.
result Improves mutual information with metadata at the cost of structural detectability.
I2C enables agents to learn efficient communication without redundancy.
problem Redundant broadcast communication in multi-agent cooperation.
method I2C learns a prior for agent-agent communication via causal inference and reinforcement learning.
result I2C reduces communication overhead and improves multi-agent cooperative performance.
Study characterizes community structure in Japanese production network.
problem Characterize community structure in a large-scale production network.
method Directed network analysis of one million Japanese firms.
result Large fraction of firms have local interactions, and community strengths are heterogeneous.
Paper explores exact recovery of communities in weighted graphs using Gaussian and exponential distributions.
problem Exact recovery of communities in weighted graphs with Gaussian and exponential distributions.
method Introduces a new semi-metric to describe conditions for exact recovery and analyzes conditions for both complete and incomplete graphs.
result Necessary and sufficient conditions for exact recovery are asymptotically tight and applicable to both complete and incomplete graphs.
HSQ reduces communication costs in federated learning.
problem High cost of communicating gradients in federated learning.
method Hyper-sphere quantization (HSQ) framework for efficient gradient compression.
result HSQ achieves O(logd) per-iteration communication cost, significantly reducing costs without compromising accuracy. A fuzzy recommender system using PageRank for community detection.
problem Improving recommendation systems for large user communities.
method Fuzzy community detection using personalized PageRank.
result The method outperforms recent recommender systems on MovieLens and FilmTrust datasets.
Generative model for creating graphs with new communities.
problem Generating graphs with a new community structure.
method Fit Gaussian mixture model to latent space data and add new clusters based on MDL principle.
result Empirically demonstrated effectiveness of GCA for generating graphs with new community structures.
Paper proposes a method to detect fair communities in graphs considering demographic attributes.
problem Inconsistent community detection violates fairness constraints for nodes with demographic attributes.
method Develops an ℓ1-regularized pseudo-likelihood approach for fair graphical model selection. result The method ensures demographic groups are fairly represented within detected communities.
New model detects communities in multiplex networks, accounting for layer dependencies.
problem Detecting communities in multiplex networks with layer-specific dependencies.
method Hierarchical Bayesian model with a hierarchical Dirichlet prior and slice sampler.
result Model automatically picks the number of communities at each layer, outperforming single-layer alternatives.
Study examines Fed's pandemic communication strategies.
problem Analyzing Federal Reserve's communication during the COVID-19 pandemic.
method Sentiment analysis, topic modeling, comparative analysis of previous crises.
result Fed's communication during the pandemic focused on financial stability, market volatility, social welfare, and unconventional monetary policy.
We investigate the community structure of the global ownership network of transnational corporations. We find a pronounced organization in communities that cannot be explained by randomness. Despite the global character of this network, communities reflect first of all the geographical location of firms, while the indu…
We propose and analyze a generic method for community recovery in stochastic block models and degree corrected block models. This approach can exactly recover the hidden communities with high probability when the expected node degrees are of order logn or higher. Starting from a roughly correct community partition …
We optimize distributed learning algorithms to maintain linear convergence with limited communication.
problem Limited communication time affects the convergence of distributed learning algorithms.
method We design quantizers to compress algorithm information while preserving linear convergence and characterize communication time.
result We show how to co-design machine learning and communication protocols for optimal performance.
New research shows treating metadata as ground truth in network analysis leads to significant problems.
problem The use of metadata as ground truth in community detection leads to theoretical and practical issues.
method Theoretical analysis and statistical techniques to quantify the relationship between metadata and community structure.
result No algorithm can uniquely solve community detection, and treating metadata as ground truth is problematic.
The paper introduces curvature-based clustering algorithms for graph analysis.
problem Identifying densely connected substructures in graphs for community detection.
method Discrete Ricci curvatures and geometric flows to reveal community structure.
result The curvature-based approach can identify overlapping communities in graphs.