Join-Graph Propagation Algorithms
The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. Algorithm IJGP belongs to the class of Generalized Belief Propagation algorithms, a framework that allowed connections with approximate algorithms from statistical physics and is shown empirically to surpass the performance of mini-clustering and belief propagation, as well as a number of other state-of-the-art algorithms on several classes of networks. We also provide insight into the accuracy of iterative BP and IJGP by relating these algorithms to well known classes of constraint propagation schemes.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringSimilar Papers 제목 키워드 기반
Dynamic graph neural network for fake news detection
The widespread of fake news on social media and other platforms can bring significant damage to the harmony and stability of our society. To defend against fake news, researchers have suggested various ways of dealing …
Fake News DetectionGraph Neural NetworkA Message Passing Algorithm for the Problem of Path Packing in Graphs
We consider the problem of packing node-disjoint directed paths in a directed graph. We consider a variant of this problem where each path starts within a fixed subset of root nodes, subject to a given bound on the lengt…
Cross-Domain Recommendation via Preference Propagation GraphNet
Recommendation can be framed as a graph link prediction task naturally. The user-item interaction graph built within a single domain often suffers from high sparsity. Thus, there has been a surge of approaches to allevia…
Link PredictionTransfer LearningJoint Sparse Graph for Enhanced MIMO-AFDM Receiver Design
Affine frequency division multiplexing (AFDM) is a promising chirp-assisted multicarrier waveform for future high-mobility communications. This paper is devoted to enhanced receiver design for multiple input and multiple…
Variational InferenceConvex Combination Belief Propagation Algorithms
We present new message passing algorithms for performing inference with graphical models. Our methods are designed for the most difficult inference problems where loopy belief propagation and other heuristics fail to con…