Publications related to Linkless embedding

GRAMM: Fast CGRA Application Mapping Based on A Heuristic for Finding Graph Minors

A graph H is a minor of a second graph G if G can be transformed into H by two operations: 1) deleting nodes and/or edges, or 2) contracting edges. Coarse-grained reconfigurable array (CGRA) application mapping is closely related to the graph minor problem ...

Los Alamitos2023

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Karl Aberer, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thành Tâm Nguyên

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...

ELSEVIER SCIENCE INC2023

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Yichen Xu, Qiang Liu, Feng Yu

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

Assoc Computing Machinery2023

Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings

Mackenzie Mathis, Steffen Schneider

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for dimensionality reduction of time-series using contrastive learning. A method can include receiving multidimensional input time series data that includes ...

2023

A full characterization of invariant embeddability of unimodular planar graphs

Laszlo Marton Toth

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 ...

WILEY2023

Computational Exploration of Multistable Elastic Knots

Mark Pauly, Michele Vidulis, Yingying Ren

We present an algorithmic approach to discover, study, and design multistable elastic knots. Elastic knots are physical realizations of closed curves embedded in 3-space. When endowed with the material thickness and bending resistance of a physical wire, t ...

2023

Graph Embedding for Retrieval

Chi Thang Duong

Information retrieval (IR) systems such as search engines are important for people to find what they need among the tremendous amount of data available in their organization or on the Internet. These IR systems enable users to search in a large data collec ...

EPFL2022

Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps

Aude Billard, Bernardo Fichera

Dynamical Systems (DS) are fundamental to the modeling and understanding time evolving phenomena, and have application in physics, biology and control. As determining an analytical description of the dynamics is often difficult, data-driven approaches are ...

Microtome Publishing2022

Random walks and forbidden minors III: poly(d epsilon(-1))-time partition oracles for minor-free graph classes

Akash Kumar

Consider the family of bounded degree graphs in any minor-closed family (such as planar graphs). Let d be the degree bound and n be the number of vertices of such a graph. Graphs in these classes have hyperfinite decompositions, where, one removes a small ...

IEEE COMPUTER SOC2022

Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding

Karl Aberer, Quoc Viet Hung Nguyen, Thanh Trung Huynh, Thành Tâm Nguyên, Chi Thang Duong

The heterogeneity of today's Web sources requires information retrieval (IR) systems to handle multi-modal queries. Such queries define a user's information needs by different data modalities, such as keywords, hashtags, user profiles, and other media. Rec ...

IEEE COMPUTER SOC2022

Efficient Streaming Subgraph Isomorphism with Graph Neural Networks

Karl Aberer, Quoc Viet Hung Nguyen, Chi Thang Duong, Trung-Dung Hoang

Queries to detect isomorphic subgraphs are important in graph-based data management. While the problem of subgraph isomorphism search has received considerable attention for the static setting of a single query, or a batch thereof, existing approaches do n ...

ASSOC COMPUTING MACHINERY2021

Partition and Code: learning how to compress graphs

Andreas Loukas, Michael Bronstein, Nikolaos Karalias, Georgios Bouritsas

Can we use machine learning to compress graph data? The absence of ordering in graphs poses a significant challenge to conventional compression algorithms, limiting their attainable gains as well as their ability to discover relevant patterns. On the other ...

2021

Scalable 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 MACHINERY2021

Low-Rank Subspaces for Unsupervised Entity Linking

Robert West, Akhil Arora, Alberto Garcia Duran

Entity linking is an important problem with many applications. Most previous solutions were designed for settings where annotated training data is available, which is, however, not the case in numerous domains. We propose a light-weight and scalable entity ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Graph Embeddings for One-pass Processing of Heterogeneous Queries

Karl Aberer, Quoc Viet Hung Nguyen, Chi Thang Duong

Effective information retrieval (IR) relies on the ability to comprehensively capture a user's information needs. Traditional IR systems are limited to homogeneous queries that define the information to retrieve by a single modality. Support for heterogene ...

IEEE COMPUTER SOC2020

Realizability of planar point embeddings from angle measurements

Majed El Helou, Adam James Scholefield, Frederike Dümbgen

Localization of a set of nodes is an important and a thoroughly researched problem in robotics and sensor networks. This paper is concerned with the theory of localization from inner-angle measurements. We focus on the challenging case where no anchor loca ...

IEEE2020

Fast and Accurate Efficient Streaming Subgraph Isomorphism

Karl Aberer, Quoc Viet Hung Nguyen, Chi Thang Duong, Trung-Dung Hoang

Queries to detect isomorphic subgraphs are important in graph-based data management. While the problem of subgraph isomorphism search has received considerable attention for the static setting of a single query, or a batch thereof, existing approaches do n ...

2020

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

GEFF: Graph embedding for functional fingerprinting

Enrico Amico

It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate ...

2020

Improving speech embedding using crossmodal transfer learning with audio-visual data

Jean-Marc Odobez

Learning a discriminative voice embedding allows speaker turns to be compared directly and efficiently, which is crucial for tasks such as diarization and verification. This paper investigates several transfer learning approaches to improve a voice embeddi ...

2019