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LINE first- and second-order network embeddings

Learns direct-edge and neighborhood-context embeddings using negative sampling and weight-proportional edge sampling, then combines separately trained proximity representations.

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Learns direct-edge and neighborhood-context embeddings using negative sampling and weight-proportional edge sampling, then combines separately trained proximity representations.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

Six source authors; internship mark not shared-lead mark. First-order objective applies to undirected graphs; second-order supports directed graphs. Practical method concatenates separately trained embeddings rather than solving a claimed joint objective. Empirical near-linear work is not a universal convergence/runtime theorem; isolated unseen vertices need extra information.

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People and systems

Jian Tang; Meng Qu; Mingzhe Wang; Ming Zhang; Jun Yan; Qiaozhu Mei

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

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English catalogue narrative

LINE first- and second-order network embeddings

Learns direct-edge and neighborhood-context embeddings using negative sampling and weight-proportional edge sampling, then combines separately trained proximity representations.

Scientific scope

Six source authors; internship mark not shared-lead mark. First-order objective applies to undirected graphs; second-order supports directed graphs. Practical method concatenates separately trained embeddings rather than solving a claimed joint objective. Empirical near-linear work is not a universal convergence/runtime theorem; isolated unseen vertices need extra information.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Jian Tang; Meng Qu; Mingzhe Wang; Ming Zhang; Jun Yan; Qiaozhu Mei

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

Original sources