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Classifying Dyads for Militarized Conflict Analysis
Related publications (33)
The connection of the acyclic disconnection and feedback arc sets - On an open problem of Figueroa et al.
We examine the connection of two graph parameters, the size of a minimum feedback arcs set and the acyclic disconnection. A feedback arc set of a directed graph is a subset of arcs such that after deletion the graph becomes acyclic. The acyclic disconnecti ...
Elsevier2024Equivariant Neural Architectures for Representing and Generating Graphs
Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entitie ...
EPFL2023A full characterization of invariant embeddability of unimodular planar graphs
When can a unimodular random planar graph be drawn in the Euclidean or the hyperbolic plane in a way that the distribution of the random drawing is isometry-invariant? This question was answered for one-ended unimodular graphs in Benjamini and Timar, using ...
WILEY2023Maximum Independent Set: Self-Training through Dynamic Programming
Volkan Cevher, Grigorios Chrysos, Efstratios Panteleimon Skoulakis
This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly construc ...
2023Distributed Graph Learning With Smooth Data Priors
Pascal Frossard, Mireille El Gheche, Isabela Cunha Maia Nobre
Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the graph signals, namely the data that live ...
IEEE2022Representation Learning for Multi-relational Data
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 the ...
EPFL2021Representing graphs through data with learning and optimal transport
Graphs offer a simple yet meaningful representation of relationships between data. Thisrepresentation is often used in machine learning algorithms in order to incorporate structuralor geometric information about data. However, it can also be used in an inv ...
EPFL2021Scalable Robust Graph Embedding with Spark
Karl Aberer, Quoc Viet Hung Nguyen, Chi Thang Duong, Trung-Dung Hoang
Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to larg ...
ASSOC COMPUTING MACHINERY2021Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations
Mrinmaya Sachan, Mattia Atzeni
Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowledge to improve the efficiency of RL ...
ASSOC COMPUTATIONAL LINGUISTICS-ACL2021Graph Learning Under Partial Observability
Ali H. Sayed, Augusto José Rabelo Almeida Santos
Many optimization, inference, and learning tasks can be accomplished efficiently by means of decentralized processing algorithms where the network topology (i.e., the graph) plays a critical role in enabling the interactions among neighboring nodes. There ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020