Glossario IA
Il dizionario completo dell'Intelligenza Artificiale
Degree Centrality
Fundamental measure that quantifies the importance of a node by counting the number of incident edges, thus indicating its level of direct connectivity in the network.
Betweenness Centrality
Indicator that evaluates a node's influence by measuring its frequency of appearance on the shortest paths between all pairs of nodes in the graph.
Closeness Centrality
Metric that evaluates a node's access efficiency to others by calculating the average of the shortest distances to all other nodes in the network.
Eigenvector Centrality
Influence measure based on recursive connectivity, where a node's importance depends on the importance of the nodes it is connected to.
Clustering Coefficient
Indicator measuring a node's tendency to form triangles with its neighbors, thus quantifying the local density of connections in its immediate neighborhood.
PageRank
Iterative algorithm that assigns importance scores to nodes based on the quality and quantity of incoming links, originally developed for web page ranking.
Node Embeddings
Dense, low-dimensional vector representations of graph nodes, capturing their structural and relational properties in a continuous space.
Node2Vec
Node representation learning algorithm using biased random walks to simultaneously capture structural and proximity equivalences in graphs.
DeepWalk
Unsupervised approach that generates node embeddings by combining truncated random walks with the Skip-gram model from natural language processing.
Graph Neural Networks
Deep learning architecture designed to directly process graph structures, propagating and aggregating information through connections between nodes.
Adjacency matrix
Square matrix representation of a graph where each element indicates the presence or absence of an edge between corresponding nodes.
Graph spectrum
Set of eigenvalues of the graph's adjacency or Laplacian matrix, providing global information about its structure and topological properties.
Graph motifs
Recurrent and statistically significant subgraphs appearing more frequently than in random graphs, revealing fundamental structural patterns.
Community detection
Algorithmic process aimed at identifying groups of nodes that are densely connected to each other and weakly connected to other groups in the graph.
Jaccard similarity
Metric measuring the degree of similarity between two nodes by calculating the ratio between the size of the intersection and the size of the union of their respective neighborhoods.
Walk2Vec
Embedding technique generating vector representations by capturing random walk patterns to preserve node proximity and structural properties.
Modularity
Measure quantifying the quality of a community partition by evaluating the density of intra-community connections relative to a random null model.
Topological descriptors
Set of quantitative characteristics describing the intrinsic structural properties of a graph, including degree distribution and characteristic paths.
Katz centrality
Centrality measure weighting all paths between nodes with an attenuation factor, giving more importance to short paths than to long paths.
Graphlets
Small non-isomorphic induced subgraphs serving as structural primitives to finely characterize the local environment of nodes in complex networks.