Group-Node Attention for Community Evolution Prediction
Communities in social networks evolve over time as people enter and leave the network and their activity behaviors shift. The task of predicting structural changes in communities over time is known as community evolution prediction. Existing work in this area has focused on the development of frameworks for defining events while using traditional classification methods to perform the actual prediction. We present a novel graph neural network for predicting community evolution events from structural and temporal information. The model (GNAN) includes a group-node attention component which enables support for variable-sized inputs and learned representation of groups based on member and neighbor node features. A comparative evaluation with standard baseline methods is performed and we demonstrate that our model outperforms the baselines. Additionally, we show the effects of network trends on model performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph Neural NetworkPredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
GRADE: Graph Dynamic Embedding
Representation learning of static and more recently dynamically evolving graphs has gained noticeable attention. Existing approaches for modelling graph dynamics focus extensively on the evolution of individual nodes ind…
Community DetectionDynamic Community DetectionDynamic Link PredictionLink Prediction+2Enhance Ambiguous Community Structure via Multi-strategy Community Related Link Prediction Method with Evolutionary Process
Most real-world networks suffer from incompleteness or incorrectness, which is an inherent attribute to real-world datasets. As a consequence, those downstream machine learning tasks in complex network like community det…
AttributeCommunity DetectionLink PredictionDeep Amortized Relational Model with Group-Wise Hierarchical Generative Process
In this paper, we propose Deep amortized Relational Model (DaRM) with group-wise hierarchical generative process for community discovery and link prediction on relational data (e.g., graph, network). It provides an effic…
Community DetectionLink PredictionBoosting Multitask Learning on Graphs through Higher-Order Task Affinities
Predicting node labels on a given graph is a widely studied problem with many applications, including community detection and molecular graph prediction. This paper considers predicting multiple node labeling functions o…
Community DetectionNode ClassificationEfficient Personalized Community Detection via Genetic Evolution
Personalized community detection aims to generate communities associated with user need on graphs, which benefits many downstream tasks such as node recommendation and link prediction for users, etc. It is of great impor…
Community DetectionLink Prediction