ComputeInfluencegraph

ComputeInfluencegraph is a computational routine used in network analysis to derive a directed, weighted graph that encodes estimated influence relationships among a set of entities. Given observational data such as time-stamped interactions, activity logs, or feature vectors, the function constructs nodes for each entity and estimates an influence score from each potential source to each target. The resulting influence graph can help identify key influencers, diffusion pathways, and aspects of causal structure in the observed system.

Implementation typically proceeds in several steps. First, data are preprocessed to align time scales and handle missing values. Then, for each pair of nodes (i, j) the influence of i on j is estimated using methods such as Granger causality, transfer entropy, mutual information, or regression-based scores. Statistical significance tests or bootstrapping are often used to assess whether the observed influence is unlikely to arise by chance. A threshold is applied to convert influence scores into edges, and weights may be normalized or kept as raw scores. Finally, the graph can be pruned to enforce sparsity or to reflect practical constraints like maximum out-degree.

The output is a directed, weighted graph where edges point from influencers to influenced nodes and weights reflect estimated influence strength. The graph may be augmented with metadata such as time lags, confidence intervals, or edge types to capture different influence modalities.

Applications include studying information diffusion in social networks, epidemiological spread, marketing and recommendation effects, and sensor or neural networks. Limitations include sensitivity to data quality, choices of influence metrics, and computational complexity for large networks. See also Granger causality, transfer entropy, influence maximization, and causal network inference.