no code implementations • 23 May 2024 • Fabian Spaeh, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis
This study introduces a novel approach for learning mixtures of Markov chains, a critical process applicable to various fields, including healthcare and the analysis of web users.
no code implementations • 27 Feb 2024 • Fabian Spaeh, Charalampos E. Tsourakakis
A notable unresolved query in stochastic processes is learning mixtures of continuous-time Markov chains (CTMCs).
1 code implementation • 9 Feb 2023 • Fabian Spaeh, Charalampos E. Tsourakakis
Finally, we empirically observe that combining an EM-algorithm with our method performs best in practice, both in terms of reconstruction error with respect to the distribution of 3-trails and the mixture of Markov Chains.
1 code implementation • NeurIPS 2021 • Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis
We prove that subject to a bounded overlap condition, which ensures that the model does not simply memorize a single graph, edge independent models are inherently limited in their ability to generate graphs with high triangle and other subgraph densities.
1 code implementation • 17 Feb 2021 • Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis
Our findings are a step towards a more rigorous understanding of exactly what information embeddings encode about the input graph, and why this information is useful for learning tasks.
1 code implementation • NeurIPS 2020 • Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis
In this work we show that the results of Seshadhri et al. are intimately connected to the model they use rather than the low-dimensional structure of complex networks.
no code implementations • 21 Sep 2019 • Kasper Green Larsen, Michael Mitzenmacher, Charalampos E. Tsourakakis
The goal is to recover $n$ discrete variables $g_i \in \{0, \ldots, k-1\}$ (up to some global offset) given noisy observations of a set of their pairwise differences $\{(g_i - g_j) \bmod k\}$; specifically, with probability $\frac{1}{k}+\delta$ for some $\delta > 0$ one obtains the correct answer, and with the remaining probability one obtains a uniformly random incorrect answer.
no code implementations • 25 Apr 2019 • Konstantinos Sotiropoulos, John W. Byers, Polyvios Pratikakis, Charalampos E. Tsourakakis
This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions.
1 code implementation • 24 Jan 2018 • Rediet Abebe, Jon Kleinberg, David Parkes, Charalampos E. Tsourakakis
This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people's intrinsic opinions, it is also natural to consider interventions that modify people's susceptibility to persuasion.
1 code implementation • 23 Jan 2018 • Jeremy G. Hoskins, Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis
In this work we consider a privacy threat to a social network in which an attacker has access to a subset of random walk-based node similarities, such as effective resistances (i. e., commute times) or personalized PageRank scores.
3 code implementations • 28 Dec 2017 • Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis
We perform an empirical study of our proposed methods on synthetic and real-world data that verify their value as mining tools to better understand the trade-off between of disagreement and polarization.
1 code implementation • 19 Sep 2017 • Charalampos E. Tsourakakis, Michael Mitzenmacher, Kasper Green Larsen, Jarosław Błasiok, Ben Lawson, Preetum Nakkiran, Vasileios Nakos
The {\em edge sign prediction problem} aims to predict whether an interaction between a pair of nodes will be positive or negative.
1 code implementation • 17 Sep 2016 • Jarosław Błasiok, Charalampos E. Tsourakakis
We verify experimentally the efficiency of our method on numerous real-world datasets, where we find that our method ($<$10 secs) is more than 3\, 000$\times$ faster than the state-of-the-art method \cite{hedge2015} ($>$9 hours) on medium scale datasets with 60\, 000 data points in 784 dimensions.