Papers Dynamic graph embedding
“Dynamic graph embedding” 태그가 달린 논문 24편 · 필터 해제
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers
Dynamic graph embedding has emerged as an important technique for modeling complex time-evolving networks across diverse domains. While transformer-based models have shown promise in capturing long-range dependencies in …
Computational EfficiencyDynamic graph embeddingGraph EmbeddingGraph Representation Learning+4Non-Progressive Influence Maximization in Dynamic Social Networks
The influence maximization (IM) problem involves identifying a set of key individuals in a social network who can maximize the spread of influence through their network connections. With the advent of geometric deep lear…
Deep Reinforcement LearningDynamic graph embeddingGraph Embeddingreinforcement-learning+1Local Intrinsic Dimensionality for Dynamic Graph Embeddings
The notion of local intrinsic dimensionality (LID) has important theoretical implications and practical applications in the fields of data mining and machine learning. Recent research efforts indicate that LID measures d…
Dynamic graph embeddingGraph EmbeddingEmpowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories
We developed DyGETViz, a novel framework for effectively visualizing dynamic graphs (DGs) that are ubiquitous across diverse real-world systems. This framework leverages recent advancements in discrete-time dynamic graph…
Dynamic graph embeddingEpidemiologyGraph EmbeddingToward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach
Recent studies successfully learned static graph embeddings that are structurally fair by preventing the effectiveness disparity of high- and low-degree vertex groups in downstream graph mining tasks. However, achieving …
Dynamic graph embeddingFairnessGraph EmbeddingGraph MiningDTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs
Dynamic text-attributed graphs (DyTAGs) are prevalent in various real-world scenarios, where each node and edge are associated with text descriptions, and both the graph structure and text descriptions evolve over time. …
Dynamic graph embeddingEdge ClassificationGraph LearningLink Prediction+2Valid Conformal Prediction for Dynamic GNNs
Dynamic graphs provide a flexible data abstraction for modelling many sorts of real-world systems, such as transport, trade, and social networks. Graph neural networks (GNNs) are powerful tools allowing for different kin…
Conformal PredictionDynamic graph embeddingGraph EmbeddingPrediction+2DySuse: Susceptibility Estimation in Dynamic Social Networks
Influence estimation aims to predict the total influence spread in social networks and has received surged attention in recent years. Most current studies focus on estimating the total number of influenced users in a soc…
Dynamic graph embeddingGraph EmbeddingTransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers
Dynamic graph embedding has emerged as a very effective technique for addressing diverse temporal graph analytic tasks (i.e., link prediction, node classification, recommender systems, anomaly detection, and graph genera…
Anomaly DetectionComputational EfficiencyDynamic graph embeddingGraph Embedding+5ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives
This paper presents ATEM, a novel framework for studying topic evolution in scientific archives. ATEM is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and cit…
ArticlesDynamic graph embeddingDynamic Topic ModelingGraph EmbeddingParameter-free Dynamic Graph Embedding for Link Prediction
Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time. There are two crucial factors when modelling user preferences for link prediction in dynamic interaction gra…
AttributeDynamic graph embeddingGraph EmbeddingLink Prediction+1Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution
Dynamic graphs refer to graphs whose structure dynamically changes over time. Despite the benefits of learning vertex representations (i.e., embeddings) for dynamic graphs, existing works merely view a dynamic graph as a…
Dynamic graph embeddingGraph EmbeddingGraph MiningWalkingTime: 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 glob…
Dynamic graph embeddingGraph EmbeddingKnowledge GraphsNetwork Embedding+1DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs
Dynamic graph embedding has gained great attention recently due to its capability of learning low dimensional graph representations for complex temporal graphs with high accuracy. However, recent advances mostly focus on…
DiversityDynamic graph embeddingGraph EmbeddingTriplet+1Data-driven Smart Ponzi Scheme Detection
A smart Ponzi scheme is a new form of economic crime that uses Ethereum smart contract account and cryptocurrency to implement Ponzi scheme. The smart Ponzi scheme has harmed the interests of many investors, but research…
Dynamic graph embeddingFeature EngineeringGraph EmbeddingDyGCN: Dynamic Graph Embedding with Graph Convolutional Network
Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes, has received significant attention recently. Recent years have witnessed a surge of efforts made on static graphs, among which …
Dynamic graph embeddingGraph EmbeddingFeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding
Dynamic graphs arise in a plethora of practical scenarios such as social networks, communication networks, and financial transaction networks. Given a dynamic graph, it is fundamental and essential to learn a graph repre…
Dynamic graph embeddingGraph EmbeddingA 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+4Understanding graph embedding methods and their applications
Graph analytics can lead to better quantitative understanding and control of complex networks, but traditional methods suffer from high computational cost and excessive memory requirements associated with the high-dimens…
Community DetectionDynamic graph embeddingGraph EmbeddingLink Prediction+1K-Core based Temporal Graph Convolutional Network for Dynamic Graphs
Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information. However, many existing methods focus on…
Dynamic graph embeddingGraph EmbeddingGraph Representation LearningLink Prediction+1