24 papers · ranked by Valyu relevance
Claudio D. T. Barros, Matheus R. F. Mendonça, Alex Borges Vieira, Artur Ziviani
'Artur Ziviani'] Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization. However, many real-world networks present dynamic behavior, including topological evolution, feature…
Ziyu Liao, Tao Liu, Yue He, Longlong Lin + 1 more
Graph representation learning aims to map nodes or edges within a graph using low-dimensional vectors, while preserving as much topological information as possible. During past decades, numerous algorithms for graph representation learning have emerged. Among them, proximity matrix representation methods have been…
Meng‐Lin Yang, Min Zhou, Marcus Kalander, Zengfeng Huang + 1 more
'Irwin King'] Representation learning over temporal networks has drawn considerable attention in recent years. Efforts are mainly focused on modeling structural dependencies and temporal evolving regularities in Euclidean space which, however, underestimates the inherent complex and hierarchical properties in many…
Da Xu, Chuanwei Ruan, Evren Körpeoğlu, Sushant Kumar + 1 more
Inductive representation learning on temporal graphs is an important step toward salable machine learning on real-world dynamic networks. The evolving nature of temporal dynamic graphs requires handling new nodes as well as capturing temporal patterns. The node embeddings, which are now functions of time, should…
Di Jin, Sungchul Kim, Ryan A. Rossi, Danai Koutra
We introduce a general framework for leveraging graph stream data for temporal prediction-based applications. Our proposed framework includes novel methods for learning an appropriate graph time-series representation, modeling and weighting the temporal dependencies, and generalizing existing embedding methods for such…
Shaohan Wei, Marco Piangerelli
Graph embedding has gained significant popularity due to its ability to represent large-scale graph data by mapping nodes to a low-dimensional space. However, most of the existing research in this field has focused on transductive learning, where fixed node embeddings are generated by training the entire graph. This…
Lorenzo Dall’Amico, Alain Barrat, Ciro Cattuto
Temporal graphs are commonly used to represent time-resolved relations between entities in many natural and artificial systems. Many techniques were devised to investigate the evolution of temporal graphs by comparing their state at different time points. However, quantifying the similarity between temporal graphs as a…
Paola Lecca, Michela Lecca
Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually…
Khushnood Abbas, Alireza Abbasi, Shi Dong, Ling Niu + 3 more
'Bolun Chen' 'José F. F. Mendes'] Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives. Predicting future links among the nodes in these dynamic networks has many practical…
Stephen Bonner, John D. Brennan, Ibad Kureshi, Georgios Theodoropoulos + 2 more
'Georgios Theodoropoulos' 'A. Stephen McGough' 'Bogusław Obara'] Abstract—Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets. Many real-world phenomenon are dynamic in nature, meaning that any graph used to represent them is inherently temporal.…
Stephen Bonner, Amir Atapour-Abarghouei, Philip T. Jackson, John D. Brennan + 4 more
'John D. Brennan' 'Ibad Kureshi' 'Georgios Theodoropoulos' 'A. Stephen McGough' 'Bogusław Obara'] Abstract—Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines. However, when graphs are used as input to machine learning models, this rich…
Most Tahmina Rahman, Mohammad Al Olaimat, Serdar Bozdag
Electronic Health Records (EHRs) contain vast amounts of longitudinal patient medical history data, making them highly informative for early disease prediction. Numerous computational methods have been developed to leverage EHR data; however, many process multiple patient records simultaneously, resulting in high…
Authors not listed
Accurate prediction of chemical reaction yields remains essential for accelerating synthesis optimization, yet current machine learning models face critical limitations in capturing temporal dynamics, providing calibrated uncertainty estimates, and explicitly modeling reactant-to-product transformations. Here we…
Mohamed Radwan, Pedro G. Lind, Anis Yazidi
In this study, we investigate the use of temporal dynamics in brain connectivity for the classification of electroencephalography (EEG) signals using dynamic Graph Neural Networks (GNNs). Our methods are applied to several large-scale EEG datasets focused on abnormality and epilepsy detection. The implemented models…
Simone Piaggesi, André Panisson
Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms. Since many real-world networks are inherently dynamic, with interactions among nodes changing over time, these techniques can be defined both…
Dongqi Fu, Jingrui He
Graph structures have attracted much research attention for carrying complex relational information. Based on graphs, many algorithms and tools are proposed and developed for dealing with real-world tasks such as recommendation, fraud detection, molecule design, etc. In this paper, we first discuss three topics of…
Jennifer Stiso, Christopher W. Lynn, Ari E. Kahn, Vinitha Rangarajan + 8 more
Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming cognitive maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an…
Minzhang Zheng, Sergii Domanskyi, Carlo Piermarocchi, George I. Mias
Temporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems: (i) How to create the appropriate network to reflect the characteristics of biological time series. (ii) How to detect characteristic…
Tatsuya Haga, Tomoki Fukai
Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are only…
WH Thompson, G Kastrati, K Finc, J Wright + 2 more
Brain activity can be modelled as a temporal network of interconnected regions. Recently, in network neuroscience, temporal network models have gained popularity and their network properties have been related to cognition and behaviour. Here we demonstrate that calculating nodal properties that are dependent on…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Matthew Bailey, Mark Wilson
One of the critical tools of persistent homology is the persistence diagram. We demonstrate the applicability of a persistence diagram showing the existence of topological features (here rings in a 2D network) generated over time instead of space as a tool to analyse trajectories of biological networks. We show how the…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…