Includes:
 + Node degree.
 + Node centrality.
 + The clustering coefficient.
 + Closed triangles, ego graphs, and motifs.



Node-level statistics and features


+ Cross-entropy with negative sampling
+ Max-margin loss

Loss functions

Graphical methods abstract the dependencies between entities, text and relations in the form of directed acyclic graphs, and then use inference models to identify the correct relations.

Hamilton College

Statistical methods bring better coverage and require less human
efforts. Thus statistical relation extraction (SRE) has been extensively studied.

Statistical Relation Extraction Models

Han, X. (n.d.). More Data, More Relations, More Context and More Openness:
A Review and Outlook for Relation Extraction *, Retrieved June 4, 2022, from https://arxiv.org/pdf/2004.03186.pdf

More Relations, More Context and More Openness:
A Review and Outlook for Relation Extraction

Feature-based methods design lexical, syntactic and semantic features for
entity pairs and their corresponding context, and then input these features into relation classifiers.

Feature-based Methods

Due to the wide use of support vector machines (SVM), kernel-based methods have been widely explored, which design kernel functions for SVM to measure the similarities between relation representations and textual instances.

Kernel-based Methods

Graphical Methods 

There are efforts in encoding text into low-dimensional semantic spaces and extracting relations from textual embeddings.

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