Concept

Directed acyclic graph

Related publications (260)

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On the Experimental Transferability of Spectral Graph Convolutional Networks

Spectral Graph Convolutional Networks (GCNs) are generalisations of standard convolutional for graph-structured data using the Laplacian operator. Recent work has shown that spectral GCNs have an intrinsic transferability. This work verifies this by studyi ...

2020

Modeling Network Populations via Graph Distances

Sofia Charlotta Olhede

This article introduces a new class of models for multiple networks. The core idea is to parameterize a distribution on labeled graphs in terms of a Frechet mean graph (which depends on a user-specified choice of metric or graph distance) and a parameter t ...

AMER STATISTICAL ASSOC2020

Experiments with the Markoff Surface

Matthew De Courcy-Ireland, Sandy Lee

We confirm, for the primes up to 3000, the conjecture of Bourgain-Gamburd-Sarnak and Baragar on strong approximation for the Markoff surface modulo primes. For primes congruent to 3 modulo 4, we find data suggesting that some natural graphs constructed fro ...

TAYLOR & FRANCIS INC2020

Graph 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

Robust Distributed Learning

El Mahdi El Mhamdi

Whether it occurs in artificial or biological substrates, {\it learning} is a {distributed} phenomenon in at least two aspects. First, meaningful data and experiences are rarely found in one location, hence {\it learners} have a strong incentive to work t ...

EPFL2020

Ramsey numbers of ordered graphs

Jan Kyncl

An ordered graph is a pair G = (G,

ELECTRONIC JOURNAL OF COMBINATORICS2020

Learning Graph Influence From Social Interactions

Ali H. Sayed, Virginia Bordignon, Augusto José Rabelo Almeida Santos

In social learning, agents form their opinions or beliefs about certain hypotheses by exchanging local information. This work considers the recent paradigm of weak graphs, where the network is partitioned into sending and receiving components, with the for ...

IEEE2020

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

Volodymyr Miz

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 ...

EPFL2020

Graph Transform Optimization With Application to Image Compression

Pascal Frossard, Giulia Fracastoro

In this paper, we propose a new graph-based transform and illustrate its potential application to signal compression. Our approach relies on the careful design of a graph that optimizes the overall rate-distortion performance through an effective graph-bas ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Buffer Placement and Sizing for High-Performance Dataflow Circuits

Paolo Ienne, Andrea Guerrieri, Lana Josipovic, Shabnam Sheikhha

Commercial high-level synthesis tools typically produce statically scheduled circuits. Yet, effective C-to-circuit conversion of arbitrary software applications calls for dataflow circuits, as they can handle efficiently variable latencies (e.g., caches) a ...

ASSOC COMPUTING MACHINERY2020