Learning Network Embedding with Community Structural Information

Abstract

Network embedding is an effective approach to learn the low-dimensional representations of vertices in networks, aiming to capture and preserve the structure and inherent properties of networks. The vast majority of existing network embedding methods exclusively focus on vertex proximity of networks, while ignoring the network internal community structure. However, the homophily principle indicates that vertices within the same community are more similar to each other than those from different communities, thus vertices within the same community should have similar vertex representations. Motivated by this, we propose a novel network embedding framework NECS to learn the Network Embedding with Community Structural information, which preserves the high-order proximity and incorporates the community structure in vertex representation learning. We formulate the problem into a principled optimization framework and provide an effective alternating algorithm to solve it. Extensive experimental results on several benchmark network datasets demonstrate the effectiveness of the proposed framework in various network analysis tasks including network reconstruction, link prediction and vertex classification.

Community Detection Based on Density Peak Clustering Model and Multiple Attribute Decision‐Making Strategy TOPSIS

“…For the issue of the termination condition, since the community is a node group with tight internal connections and sparse external connections, if the number's gain of the Input: G(V, E), the network; Eps, the radius of the DBSCAN algorithm; Minpts, the minimal number of data points contained within the radius. (9) if ∃ v ∈ cluster and ρ(v) < θ then (10) continue; ( 11) else (12) keynodes.add (cluster); (13) end ( 14) end (15) return keynodes ALGORITHM 2: Keynodes_mine (G(V, E), Eps, Minpts). 6 Complexity external edges is greater than that for the integral edges after a node is added to the community, this node is not suitable to enter the community.…”

“…partition.add ( u, v { }); (5) else (6) partition.add ( v { }); (7) end (8) end (9) arrange community frameworks in partition in the descending order of product of density and distance of nodes in the framework; (10) for c in partition do (11) Table 1: e different situations of nonkey nodes and their most similar node, and the processing strategies.…”

“…(5) T-similarity � TOPSIS (simmat); (6) u ⟵ node with max T-similarity; (7) if (v, u) ∈ E then (8) if u in partition then (9) add v to the community in which u belongs; (10) else (11) partition.add ( v, u { }); (12) end (13) else (14) partition.add (v); ( 15) end ( 16) end (17) return partition ALGORITHM 4: Nonkeynodes_alloction (G(V, E), partition, nonkeynode). 8 Complexity…”

“…e community structure contains the organizational information in each part of the network and the interaction information between these parts, which can be of great help to the research on the underlying structure and potential functions of the actual systems. And, community detection can promote other complex network studies, like influence maximization [6][7][8], network embedding [9][10][11], feature-level fusion [12], and vulnerability assessment [13].…”

Metabolic pathway prediction using non-negative matrix factorization with improved precision

“…The community detection problem [23,30] is the task of discovering distinct groups of nodes that are densely connected. During this phase, triUMPF performs community detection to guide the learning process for pathways using binary P2P (A ∈ Z t×t ≥0 ) and E2E (B ∈ Z r×r ≥0 ) associations matrices, where each entry in these matrices is a binary value indicating an interaction among corresponding entities.…”