Link prediction is a task to estimate the probability of links between nodes in a graph.
( Image credit: Inductive Representation Learning on Large Graphs )
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In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data.
In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment.
An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large networks.
Link prediction aims to reveal missing edges in a graph.
We consider the large-scale query-document retrieval problem: given a query (e. g., a question), return the set of relevant documents (e. g., paragraphs containing the answer) from a large document corpus.
In contrast with previous work, we show that our method can generalize from training for the single-hop, link prediction task, to answering queries with more complex structures.