Semantic Random Walk for Graph Representation Learning in Attributed Graphs
Paper
•
2305.06531
•
Published
•
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.