Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 56 tok/s
Gemini 2.5 Pro 39 tok/s Pro
GPT-5 Medium 15 tok/s Pro
GPT-5 High 16 tok/s Pro
GPT-4o 99 tok/s Pro
Kimi K2 155 tok/s Pro
GPT OSS 120B 476 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

Monotone Increasing Properties and Their Phase Transitions in Uniform Random Intersection Graphs (1502.00405v1)

Published 2 Feb 2015 in physics.soc-ph, cs.DM, cs.SI, math.CO, and math.PR

Abstract: Uniform random intersection graphs have received much interest and been used in diverse applications. A uniform random intersection graph with $n$ nodes is constructed as follows: each node selects a set of $K_n$ different items uniformly at random from the same pool of $P_n$ distinct items, and two nodes establish an undirected edge in between if and only if they share at least one item. For such graph denoted by $G(n, K_n, P_n)$, we present the following results in this paper. First, we provide an exact analysis on the probabilities of $G(n, K_n, P_n)$ having a perfect matching and having a Hamilton cycle respectively, under $P_n = \omega\big(n (\ln n)5\big)$ (all asymptotic notation are understood with $n \to \infty$). The analysis reveals that just like ($k$-)connectivity shown in prior work, for both properties of perfect matching containment and Hamilton cycle containment, $G(n, K_n, P_n)$ also exhibits phase transitions: for each property above, as $K_n$ increases, the limit of the probability that $G(n, K_n, P_n)$ has the property increases from $0$ to $1$. Second, we compute the phase transition widths of $G(n, K_n, P_n)$ for $k$-connectivity (KC), perfect matching containment (PMC), and Hamilton cycle containment (HCC), respectively. For a graph property $R$ and a positive constant $a < \frac{1}{2}$, with the phase transition width $d_n(R, a)$ defined as the difference between the minimal $K_n$ ensuring $G(n, K_n, P_n)$ having property $R$ with probability at least $1-a$ or $a$, we show for any positive constants $a<\frac{1}{2}$ and $k$: (i) If $P_n=\Omega(n)$ and $P_n=o(n\ln n)$, then $d_n(KC, a)$ is either $0$ or $1$ for each $n$ sufficiently large. (ii) If $P_n=\Theta(n\ln n)$, then $d_n(KC, a)=\Theta(1)$. (iii) If $P_n=\omega(n\ln n)$, then $d_n(KC, a)=\omega(1)$. (iv) If $P_n=\omega\big(n (\ln n)5\big)$, $d_n(PMC, a)$ and $d_n(HCC, a)$ are both $\omega(1)$.

Citations (2)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube