Graph Exploration for Effective Multiagent Q-Learning

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Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs. However, research on relational representation learning has so far mostly focused on th ...

Weighted undirected graphs are a simple, yet powerful way to encode structure in data. A first question we need to address regarding such graphs is how to use them effectively to enhance machine learning problems. A second but more important question is ho ...

One of the most basic graph problems, All-Pairs Shortest Paths (APSP) is known to be solvable in n^{3-o(1)} time, and it is widely open whether it has an O(n^{3-ε}) time algorithm for ε > 0. To better understand APSP, one often strives to obtain subcubic t ...

Artists and architects often need to handle multiple constraints during design of physical constructions. We define a performative constraint as any constraint on design that is tied to the performance of the model--either during fabrication, construction, ...

The construction of a meaningful graph plays a crucial role in the emerging field of signal processing on graphs. In this paper, we address the problem of learning graph Laplacians, which is similar to learning graph topologies, such that the input data fo ...

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relat ...

Graph matching is a generalization of the classic graph isomorphism problem. By using only their structures a graph-matching algorithm finds a map between the vertex sets of two similar graphs. This has applications in the de-anonymization of social and in ...

With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectral graph theory or a spatial perspec ...

In the context of integrated transportation and other urban engineering infrastructure systems, there are many examples of markets, where consumers exhibit price-taking behaviour. While this behaviour is ubiquitous, the underlying mechanism can be captured ...

In this paper, we propose an efficient planarization algorithm and a routing algorithm dedicated to Unit Disk Graphs whose nodes are localized using the Virtual Raw Anchor Coordinate system (VRAC). Our first algorithm computes a planar 2-spanner under ligh ...

A model called the linear transform network (LTN) is proposed to analyze the compression and estimation of correlated signals transmitted over directed acyclic graphs (DAGs). An LTN is a DAG network with multiple source and receiver nodes. Source nodes tra ...

A graph is a versatile data structure facilitating representation of interactions among objects in various complex systems. Very often these objects have attributes whose measurements change over time, reflecting the dynamics of the system. This general da ...

Graph Exploration for Effective Multiagent Q-Learning

Chat with Graph Search

Representation Learning for Multi-relational Data

From data to structures : graph learning under smoothness assumptions and applications in data science

Faster Monotone Min-Plus Product, Range Mode, and Single Source Replacement Paths

Fabrication-Aware Design with Performative Criteria

Laplacian Matrix Learning for Smooth Graph Signal Representation

node2coords: Graph Representation Learning with Wasserstein Barycenters

On the Performance of Percolation Graph Matching

iPool--Information-Based Pooling in Hierarchical Graph Neural Networks

Microsimulation Framework for Urban Price-Taker Markets

Efficient graph planarization in sensor networks and local routing algorithm

Reduced-Dimension Linear Transform Coding of Correlated Signals in Networks

Dynamic pattern recognition in large-scale graphs with applications to social networks