This article introduces five powerful yet often overlooked mathematical models—Lotka‑Volterra, PageRank, SIR, Nash equilibrium, and random walk—explaining their core formulas and real‑life applications from ecology to finance and internet search.
This article presents EGES, a graph‑embedding model that incorporates side information to construct a directed user graph, apply biased random‑walk sampling, and train weighted Skip‑Gram embeddings, thereby improving large‑scale user acquisition and addressing cold‑start challenges in recommendation systems.
This paper presents StudRank, a microservice root‑cause localization method that decomposes call‑chain traces into time‑series, detects anomalies, builds an abnormal propagation graph, and applies a personalized PageRank random‑walk algorithm, achieving 84% top‑1 accuracy and a 97.6% improvement over MicroRCA on public AIOps data.
This article explains the node2vec algorithm, its mathematical foundations, biased random‑walk sampling strategy with parameters p and q, implementation details using the Alias method, and demonstrates its superior performance on node classification and visualization tasks compared with DeepWalk and LINE.
