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semantic_random_walk

updated Oct 21, 2024
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  • Semantic Random Walk for Graph Representation Learning in Attributed Graphs

    Paper • 2305.06531 • Published May 11, 2023 • 2

    Note First research paper I've seen that begins with a view of graphs that takes properties (which they call "attributes") into account: > The graph can be described as a 4-tuple G = (V, E, A, F), where V = {v1, · · · , vn} is the set of nodes; E = {(vi, vj )|vi, vj ∈ V } is the set of edges; A = {a1, · · · , am} is the set of attributes; F = {f(v1), · · · , f(vn)} denotes the map from V to A, with f(vi) ⊂ A as the set of attributes of vi.


  • IRWE: Inductive Random Walk for Joint Inference of Identity and Position Network Embedding

    Paper • 2401.00651 • Published Jan 1, 2024 • 2

  • A Latent Variable Model Approach to PMI-based Word Embeddings

    Paper • 1502.03520 • Published Feb 12, 2015 • 2
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