Upper tail behavior of the number of triangles in random graphs with constant average degree
Shirshendu Ganguly, Ella Hiesmayr, and Kyeongsik Nam

TL;DR
This paper investigates the probability decay of large deviations in the number of triangles in Erdős-Rényi graphs with constant average degree, revealing a phase transition and structural insights.
Contribution
It establishes the precise phase transition for the upper tail of triangle counts and characterizes the graph structures responsible in sub-critical and super-critical regimes.
Findings
Identifies the phase transition threshold for upper tail behavior.
Shows sub-critical tail governed by almost vertex-disjoint triangles.
Super-critical tail driven by clique-like structures.
Abstract
Let be the number of triangles in an Erd\H{o}s-R\'enyi graph on vertices with edge density where is a fixed constant. It is well known that weakly converges to the Poisson distribution with mean as . We address the upper tail problem for namely, we investigate how fast must grow, so that the probability of is not well approximated anymore by the tail of the corresponding Poisson variable. Proving that the tail exhibits a sharp phase transition, we essentially show that the upper tail is governed by Poisson behavior only when (sub-critical regime) as well as pin down the tail behavior when (super-critical regime). We further prove a structure theorem, showing that the sub-critical upper…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStochastic processes and statistical mechanics · Markov Chains and Monte Carlo Methods · Complex Network Analysis Techniques
