New communication topologies improve deep reinforcement learning efficiency.
problem Optimizing communication topology for faster and more robust learning in deep reinforcement learning.
method Introduced alternative network topologies (Erdos-Renyi random graphs) and compared their performance with fully-connected and star topologies.
result Erdos-Renyi random graphs outperform fully-connected networks in deep reinforcement learning tasks.
We introduce the concept of community trees that summarizes topological structures within a network. A community tree is a tree structure representing clique communities from the clique percolation method (CPM). The community tree also generates a persistent diagram. Community trees and persistent diagrams reveal topol…
New communication topologies improve deep reinforcement learning performance.
problem Improving performance of learning agents in distributed reinforcement learning.
method Examined four graph families for communication topologies and found Erdos-Renyi random graphs to outperform fully connected topologies.
result Erdos-Renyi random graphs can improve performance of distributed learning agents.
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.
A new topology improves decentralized learning efficiency and accuracy.
problem Finding efficient decentralized learning topologies with fast consensus and low maximum degree.
method Proposed the Base-(k+1) Graph topology for decentralized learning. result The Base-(k+1) Graph enables faster convergence and better communication efficiency than the exponential graph. MATCHA speeds up decentralized SGD by parallelizing communication.
problem Error-runtime trade-off in decentralized SGD.
method MATCHA decomposes network topology into matchings for efficient communication.
result MATCHA reduces communication time by up to 5x compared to vanilla decentralized SGD.
This work introduces novel methods to identify and compare cycles across topological objects.
problem Identifying and comparing topological features, particularly cycles, across different topological objects.
method Two complementary approaches: dendrogram-based merge-tree algorithms and Stratified Gradient Sampling.
result Transformed cycle matching into hierarchical clustering and topological optimization framework.
Study improves systemic risk assessment by considering local network environments.
problem Identifying systemic financial institutions using network metrics.
method Two-step procedure: 1) recover network communities, 2) regress vulnerability on topological measures at global, local, and aggregated levels.
result Local network metrics predict distress better than global metrics during financial crises.
CodedReduce combines tree topology and gradient coding for efficient and resilient gradient aggregation.
problem Efficient and robust gradient aggregation in distributed learning.
method CodedReduce combines tree topology and gradient coding to overcome bandwidth bottlenecks and straggler delays.
result CodedReduce achieves up to 27.2x speedup over benchmarks GC and RAR.
New topological methods for hypergraph data improve community detection and pattern recognition.
problem Community detection and pattern recognition in hypergraph data.
method Introducing a new topological space structure of hypergraph data, proposing modified nearest neighbors methods.
result Improved methods for community detection and pattern recognition in hypergraph data.
New algorithm detects community labels in networks using unlabeled data.
problem Detect community labels in networks with partially labeled data.
method Proposes an algorithm using structural similarity metrics.
result Theoretical guarantees for misclassification error.
Proposes GNAN model for detecting various network structures.
problem Detecting traditional communities in networks.
method GNAN model combining topology and node-attribute information.
result GNAN detects a broader range of network structures.
Finding meaningful communities in social network has attracted the attentions of many researchers. The community structure of complex networks reveals both their organization and hidden relations among their constituents. Most of the researches in the field of community detection mainly focus on the topological structu…
GNNs robustness in community detection is studied with various perturbations.
problem Understanding GNNs robustness in community detection tasks.
method Systematic computational evaluation of six GNN architectures on synthetic and real-world networks.
result Supervised GNNs achieve higher baseline accuracy, while DMoN shows stronger resilience to perturbations.
This paper provides new algorithms for distributed clustering for two popular center-based objectives, k-median and k-means. These algorithms have provable guarantees and improve communication complexity over existing approaches. Following a classic approach in clustering by \cite{har2004coresets}, we reduce the proble…
Improves decentralized learning by teleporting active nodes for better convergence.
problem Decentralized learning's convergence rate degrades with large node numbers.
method Activates a subset of nodes, fetches parameters from previous active nodes, updates, and performs gossip averaging on a small topology.
result Teleportation completely alleviates convergence rate degradation with proper node activation.
New research shows sparse topologies can lead to faster convergence in distributed optimization.
problem The impact of worker communication topology on convergence speed in distributed optimization.
method Consensus-based distributed optimization methods with local averaging and correction based on local data.
result Sparse topologies can lead to faster convergence in distributed optimization without communication delays.
The financial market is a complex dynamical system composed of a large variety of intricate relationships between several entities, such as banks, corporations and institutions. At the heart of the system lies the stock exchange mechanism, which establishes a time-evolving network of trades among companies and individu…
GADMM reduces communication costs in distributed machine learning.
problem Efficiently solving distributed machine learning problems with reduced communication costs.
method Group Alternating Direction Method of Multipliers (GADMM) framework.
result GADMM converges to the optimal solution for convex loss functions and is faster and more communication-efficient than state-of-the-art algorithms.
The paper proves D-SGD's stability and generalization bound, highlighting the importance of communication topology.
problem The stability and generalization of decentralized stochastic gradient descent (D-SGD).
method Theoretical analysis of D-SGD's stability and generalization bound, considering spectral gap and communication topology.
result D-SGD's generalization bound is positively correlated with the spectral gap of the communication topology.
New algorithms reduce communication for sparse mean estimation in noisy distributed systems.
problem Sparse normal means estimation with limited communication in a distributed setting.
method Two distributed algorithms for estimating a sparse mean vector with sublinear communication.
result Correct support of the sparse mean can be recovered with significantly less communication than previously required.
Flexible outlier detection using graph communities for robust performance.
problem Outlier detection in small sample size unbalanced problems.
method Local measure of label heterogeneity in a weighted graph topology.
result Overall outperforms local and global strategies in multi and single view settings.
This paper connects graph curvature to community structure.
problem Understanding the relationship between network curvature and community formation.
method Defining curvature on networks and analyzing its relation to community structure.
result Apriori bounds on the curvature of intercommunity edges.
Paper studies signal detection in noisy environments with limited communication.
problem Signal detection in Gaussian noise with 1-bit communication constraints.
method Derives lower bounds and exhibits optimal testing strategies.
result Optimal distributed testing strategies attain the derived lower bound.
HSRL learns network embeddings capturing both local and global topology.
problem Capturing both local and global topological information in network analysis.
method HSRL recursively compresses networks into smaller ones, then learns embeddings using existing methods.
result HSRL outperforms state-of-the-art methods in link prediction.
A novel decentralized deep learning algorithm using gradient-based optimization.
problem Decentralized deep learning in networked systems without a central server.
method Heavy-ball acceleration method and consensus protocol for model and gradient-momentum sharing.
result The proposed algorithm outperforms competing methods in various communication topologies.
No communication allows optimal instance-dependent regret guarantees in multi-player bandits.
problem Achieving optimal instance-dependent regret in multi-player multi-armed bandits without communication.
method Characterization of Pareto optimal trade-offs and development of an algorithm.
result Achieving optimal instance-dependent regret requires strict sub-optimality in other regimes.
RiskNet predicts penalties in unreliable communication networks using GNNs.
problem Predicting penalties in networks with unreliable resources.
method Graph Neural Network (GNN) based approach trained on random graphs.
result Precisely models penalties across various network topologies.
Inter-firm organizations, which play a driving role in the economy of a country, can be represented in the form of a customer-supplier network. Such a network exhibits a heavy-tailed degree distribution, disassortative mixing and a prominent community structure. We analyze a large-scale data set of customer-supplier re…
Efficient decentralized learning framework reduces communication costs.
problem Efficiently solve optimization problems in distributed learning networks.
method Censored and Quantized Generalized GADMM (CQ-GGADMM) framework.
result Achieves linear convergence rate under strong convexity assumptions.
The topology of the domain of outer communication for 5-dimensional stationary bi-axisymmetric black holes is classified in terms of disc bundles over the 2-sphere and plumbing constructions. In particular we find an algorithmic bijective correspondence between the plumbing of disc bundles and the rod structure formali…
Community detection algorithms are fundamental tools to understand organizational principles in social networks. With the increasing power of social media platforms, when detecting communities there are two possi- ble sources of information one can use: the structure of social network and node attributes. However struc…
Multiplex networks, a special type of multilayer networks, are increasingly applied in many domains ranging from social media analytics to biology. A common task in these applications concerns the detection of community structures. Many existing algorithms for community detection in multiplexes attempt to detect commun…
Paper analyzes D-SGD convergence with heterogeneous data and proposes topology learning.
problem Efficiently dealing with data heterogeneity in decentralized learning.
method Revisits D-SGD analysis, introduces neighborhood heterogeneity, and proposes topology learning.
result Formulates topology learning as a tractable optimization problem and demonstrates its effectiveness.
Study cooperative bandit learning with imperfect communication, achieving near-optimal performance.
problem Real-world distributed decision-making with imperfect communication.
method Proposed decentralized algorithms for three communication scenarios: stochastic networks, random delays, and adversarially corrupted rewards.
result Achieved competitive performance and near-optimal guarantees on group regret.
Study how communication and feedback graphs affect learning outcomes.
problem Understanding the impact of feedback graphs on cooperative online learning.
method Analyzed network regret in terms of the independence number of the strong product of communication and feedback graphs.
result Proved bounds for network regret and demonstrated the non-improvable nature of positive results in pathological cases.
A new method reduces communication costs in decentralized optimization.
problem Decentralized optimization with non-convex cost functions.
method LU-GT method with local updates.
result LU-GT achieves the same communication complexity as Federated Learning and maintains solution quality.
Fog learning distributes ML model training across heterogeneous devices and networks.
problem Challenges with conventional federated learning in heterogeneous networks.
method Intelligent distribution of ML model training across nodes from edge devices to cloud servers.
result Enhanced federated learning with multi-layer hybrid framework considering network, heterogeneity, and proximity.
Proves existence and uniqueness of vacuum black hole solutions in higher dimensions.
problem Existence and uniqueness of solutions to Einstein equations in higher-dimensional spacetimes.
method Develops a generalized plumbing construction and analyzes singular harmonic maps.
result Establishes existence and uniqueness for black hole solutions in (n+3)-dimensional spacetimes. This paper refines understanding of decentralized learning by considering graph topology.
problem Current theory fails to predict performance in decentralized learning settings.
method Quantifies how graph topology influences convergence in decentralized learning.
result Graph topology significantly impacts convergence in decentralized learning, contrary to spectral gap theory.
Unified analysis for decentralized SGD across various topologies and updates.
problem Analysis of decentralized SGD methods with changing topologies and local updates.
method Unified convergence analysis covering local SGD updates and adaptive network topology.
result Universal convergence rates for smooth problems, interpolating between heterogeneous and iid-data settings.
BEER accelerates decentralized nonconvex optimization to O(1/T) rate.
problem Communication bottleneck in decentralized machine learning.
method Communication-compressed algorithm with gradient tracking.
result Converges at O(1/T) rate, matching uncompressed performance. HTGCN improves community detection in dynamic, heterogeneous graphs.
problem Challenges in detecting communities in graphs with varying features and temporal dynamics.
method Designs HTGCN combining heterogeneous GCN and residual compressed aggregation for dynamic feature representation.
result HTGCN outperforms existing methods on DBLP and IMDB datasets.
In this paper, we propose a technique for time series clustering using community detection in complex networks. Firstly, we present a method to transform a set of time series into a network using different distance functions, where each time series is represented by a vertex and the most similar ones are connected. The…
This workshop about triangulations of manifolds in computational geometry and topology was held at the 2014 CG-Week in Kyoto, Japan. It focussed on computational and combinatorial questions regarding triangulations, with the goal of bringing together researchers working on various aspects of triangulations and of foste…
A communication-efficient method controls FDR in network settings.
problem Controlling FDR in networks with limited communication.
method Sample-and-Forward: a flexible procedure for multihop networks.
result Nodes can control FDR without sharing p-values, achieving power and FDR control.
The stochastic block model is a powerful tool for inferring community structure from network topology. However, it predicts a Poisson degree distribution within each community, while most real-world networks have a heavy-tailed degree distribution. The degree-corrected block model can accommodate arbitrary degree distr…
DESTRESS optimizes decentralized nonconvex optimization with optimal IFO complexity and efficient communication.
problem Decentralized nonconvex finite-sum optimization in multi-agent systems.
method DESTRESS uses stochastic recursive gradient updates, gradient tracking, and careful hyper-parameter choices to achieve optimal IFO complexity with efficient communication.
result DESTRESS matches the optimal IFO complexity of centralized algorithms while maintaining communication efficiency.