… Since triangle centrality depends only upon triangle counts, and there are O(m√m) possible
triangles in G, we show that triangle centrality can be optimally computed by our Algorithm 1…

… 2) Using Intersection Representation: We use our TC algorithm to calculate triangle
centrality by taking the local triangle count and edge support information from fullCount and …

… local edge centrality called edge antitriangle centrality for community detection. EACH can
be used for large networks since it is just based on the local edge antitriangle centrality. It is …

… for calculating the percentage triangle structures of a node is O(<d 2 >), where <d> is the
average degree of the network. The total computing complexity for our centrality measure is O(n…

… [1] introduced triangle centrality as a new centrality measure that captures the influence of
triangles on the importance of vertices. In this paper, we describe triangle centrality of a graph …

… We define triangle centrality and introduce basic observations in Sections 5 and 6. We
compare our triangle centrality to common centrality measures in Sections 7 and 8. Optimal …

… Among them, we have found semi-global centrality based … However, we have observed that
the existing semi-global centrality … method “semi-global triangular centrality”, which does not …

… Closeness centrality has been used in the analysis of social and biological networks, but …
closeness centrality). We note that the S D values (and hence values for closeness centrality) …

… of the triangle inequality for each corresponding triplet. As we show, at the local level, Gromov
centrality depends on the proportion of triangles … (absence) of triangles around a node is …

… This study aims to develop novel centrality measures applicable to networks that include …
centrality, which measures local centrality. However, triangle betweenness centrality (TBC), …