Framework of SW-Metapath2vec algorithm.

Framework of SW-Metapath2vec algorithm.

<div><p>Link prediction in heterogeneous networks is an active research topic in the field of complex network science. Recognizing the limitations of existing methods, which often overlook the varying contributions of different local structures within these networks, this study introduce...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Lang Chai (20521817) (author)
مؤلفون آخرون:	Rui Huang (141490) (author)
منشور في:	2025
الموضوعات:	Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified path traces generated offer valuable insights embedding learning process active research topic complex network science link prediction techniques scale heterogeneous networks link prediction heterogeneous networks varying contributions synthetic datasets substantial proportion results indicate random walks practical application often overlook network nodes findings contribute existing methods embedded vectors cosine similarity assigning weights analyzing large algorithm enhances
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مواد مشابهة

The precision of the SW-Metapath2vec and the benchmark algorithms on the ACM network.
حسب: Lang Chai (20521817)
منشور في: (2025)

The precision of the SW-Metapath2vec and the benchmark algorithms on the DBLP network.
حسب: Lang Chai (20521817)
منشور في: (2025)

The precision of the SW-Metapath2vec and the benchmark algorithms on the Last.fm network.
حسب: Lang Chai (20521817)
منشور في: (2025)

The AUC of SW-Metapath2vec and benchmark algorithms on DBLP with various hyper-parameters.
حسب: Lang Chai (20521817)
منشور في: (2025)

The AUC of SW-Metapath2vec and benchmark algorithms with a different fraction of nodes deleted.
حسب: Lang Chai (20521817)
منشور في: (2025)

The AUC of SW-Metapath2vec and benchmark algorithms on Last.fm with various hyper-parameters.
حسب: Lang Chai (20521817)
منشور في: (2025)

The AUC of the SW-Metapath2vec and the networks-based and embedding baselines (The test ratio is 0.3).
حسب: Lang Chai (20521817)
منشور في: (2025)

Topological properties of the two synthetic networks.
حسب: Lang Chai (20521817)
منشور في: (2025)

General information for the three real heterogeneous networks.
حسب: Lang Chai (20521817)
منشور في: (2025)

A meta-path trace for “APTPVPA” with author Travis as the initial node.
حسب: Lang Chai (20521817)
منشور في: (2025)

Three real heterogeneous complex networks.
حسب: Lang Chai (20521817)
منشور في: (2025)

The local structure of the link between node “1” and node “2”.
حسب: Lang Chai (20521817)
منشور في: (2025)

Meta-paths of the “Author-Paper-Venue” network.
حسب: Lang Chai (20521817)
منشور في: (2025)

Example of transition weights for the Node2Vec over a simple subgraph.
حسب: Adilson Vital Jr. (20135941)
منشور في: (2024)

Median quality metrics for the link prediction task within 37 graphs, ordered in descending order by AUC.
حسب: Adilson Vital Jr. (20135941)
منشور في: (2024)

Graphs names and the number of nodes and edges for the utilized networks.
حسب: Adilson Vital Jr. (20135941)
منشور في: (2024)

Correlation between prediction performance (in terms of AUC) and network properties.
حسب: Adilson Vital Jr. (20135941)
منشور في: (2024)

AUC values for each graph <i>G</i> and pair of random walks <i>w</i><sub><i>x</i></sub> and <i>w</i><sub><i>y</i></sub>.
حسب: Adilson Vital Jr. (20135941)
منشور في: (2024)

Median of the Pearson correlation distribution between the random walk pairs from the 37 graphs.
حسب: Adilson Vital Jr. (20135941)
منشور في: (2024)

Two scatter plots representing the correlations between pairs of walks.
حسب: Adilson Vital Jr. (20135941)
منشور في: (2024)

Box plot of the performance obtained for the 37 networks and considering different random walks.
حسب: Adilson Vital Jr. (20135941)
منشور في: (2024)

Example of the preprocessing steps applied to the networks.
حسب: Adilson Vital Jr. (20135941)
منشور في: (2024)

Methodology used in the work.
حسب: Adilson Vital Jr. (20135941)
منشور في: (2024)

Gating-Enhanced Hierarchical Structure Learning in Hyperbolic Space and Multi-scale Neighbor Topology Learning in Euclidean Space for Prediction of Microbe-Drug Associations
حسب: Ping Xuan (195862)
منشور في: (2024)

Spherical <i>k</i>-means, achieved scores under diverse settings of the algorithm; the highest values in 10 runs.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

<i>K</i>-based clustering, scores (rows) under diverse settings of spherical <i>k</i>-means algorithm (columns), when the number of dimensions used (columns) r = 7155; the highest values in 10 runs.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

<i>K</i>-based clustering, scores (rows) under diverse settings of spherical <i>k</i>-means algorithm (columns), when the number of dimensions used (columns) r = 10; the highest values in 10 runs.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

Network structure of the two Word2vec models.
حسب: Xinle Zheng (21493581)
منشور في: (2025)

Is true clustering implied with -based GSC? Number of elements in correct clusters: 1900, incorrectly clustered: 100 = errors: 5%.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

Is true clustering implied with our -based method? Number of elements in correct clusters: 1999, incorrectly clustered: 1 = errors: 0.05%.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

Does -based GSC imply our -based GSC? Number of elements in correct clusters: 1900, incorrectly clustered: 100 = errors: 5%.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

Is true clustering implied with our K-based method? Number of elements in correct clusters: 1800, incorrectly clustered: 200 = errors: 10%.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

Does our K-based method imply L-based GSC? Number of elements in correct clusters: 1800, incorrectly clustered: 200 = errors: 10%.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

Does L-based GSC imply our K-based GSC? Number of elements in correct clusters: 1936, incorrectly clustered: 64 = errors: 3.2%.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

Best F-score for predicting spectral clustering and spherical clustering by <i>K</i>-based clustering for r = 3754.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

<i>L</i>-based spectral clustering scores under diverse settings of affinity parameter (column names).
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

Errors in reconstructing the <i>K</i> (errorK) and <i>L</i> (errorL) matrix resp.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

Confusion matrix; clsLbased—Clusters generated from <i>L</i>-embedding, clsTrue—The true clusters, number of elements in correct clusters 614 incorrectly clustered: 1016 errors: 62.33129%.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

Confusion matrix; clsKbased—Clusters generated from <i>K</i>-embedding, clsTrue—The true clusters, number of elements in correct clusters: 1214 incorrectly clustered: 416, errors: 25.52147%.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

Confusion matrix; clsKbased—Clusters generated from <i>K</i>-embedding, clsTrue—The true clusters, number of elements in correct clusters: 1984, incorrectly clustered: 16, error: 0.8%.
حسب: Bartłomiej Starosta (15195018)
منشور في: (2025)

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