WalkingTime: Dynamic Graph Embedding Using Temporal-Topological Flows
Increased attention has been paid over the last four years to dynamic network embedding. Existing dynamic embedding methods, however, consider the problem as limited to the evolution of a topology over a sequence of global, discrete states. We propose a novel embedding algorithm, WalkingTime, based on a fundamentally different handling of time, allowing for the local consideration of continuously occurring phenomena; while others consider global time-steps to be first-order citizens of the dynamic environment, we hold flows comprised of temporally and topologically local interactions as our primitives, without any discretization or alignment of time-related attributes being necessary. Keywords: dynamic networks , representation learning , dynamic graph embedding , time-respecting paths , temporal-topological flows , temporal random walks , temporal networks , real-attributed knowledge graphs , streaming graphs , online networks , asynchronous graphs , asynchronous networks , graph algorithms , deep learning , network analysis , datamining , network science
Code (0)
등록된 구현이 없습니다.
Tasks
Dynamic graph embeddingGraph EmbeddingKnowledge GraphsNetwork EmbeddingRepresentation LearningSimilar Papers 제목 키워드 기반
Inductive Representation Learning on Temporal Graphs
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…
Graph AttentionGraph EmbeddingLink PredictionNode Classification+1A Survey on Embedding Dynamic Graphs
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…
Anomaly DetectionDynamic graph embeddingDynamic Link PredictionGraph Embedding+4Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy
We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-sta…
Topological Data AnalysisLearning Persistent Community Structures in Dynamic Networks via Topological Data Analysis
Dynamic community detection methods often lack effective mechanisms to ensure temporal consistency, hindering the analysis of network evolution. In this paper, we propose a novel deep graph clustering framework with temp…
ClusteringCommunity DetectionDynamic Community DetectionGraph Clustering+1Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models
Graph representation learning for static graphs is a well studied topic. Recently, a few studies have focused on learning temporal information in addition to the topology of a graph. Most of these studies have relied on …
DecoderGraph ClassificationGraph Representation LearningRepresentation Learning