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Found 12 papers, 9 papers with code
Markovletics: Methods and A Novel Application for Learning Continuous-Time Markov Chain Mixtures
A notable unresolved query in stochastic processes is learning mixtures of continuous-time Markov chains (CTMCs).
Learning Mixtures of Markov Chains with Quality Guarantees
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.
On the Power of Edge Independent Graph Models
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.
DeepWalking Backwards: From Embeddings Back to Graphs
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.
Node Embeddings and Exact Low-Rank Representations of Complex Networks
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.
Optimal Learning of Joint Alignments with a Faulty Oracle
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.
TwitterMancer: Predicting Interactions on Twitter Accurately
This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions.
Opinion Dynamics with Varying Susceptibility to Persuasion
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.
Learning Networks from Random Walk-Based Node Similarities
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.
Minimizing Polarization and Disagreement in Social Networks
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.
Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
The {\em edge sign prediction problem} aims to predict whether an interaction between a pair of nodes will be positive or negative.
ADAGIO: Fast Data-aware Near-Isometric Linear Embeddings
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.
