arXiv subjects

Narae Ryu

Publications and source records attributed to Narae Ryu.

3 recordsLinked to original sources

arXiv2024

Non-backtracking Graph Neural Networks

The celebrated message-passing updates for graph neural networks allow representing large-scale graphs with local and computationally tractable updates. However, the updates suffer from backtracking, i.e., a message flowing through the same edge twice and revisiting the previously visited node. Since the number of message flows increases exponentially with the number of updates, the redundancy in local updates prevents the graph neural network from accurately recognizing a particular message flow relevant for downstream tasks. In this work, we propose to resolve such a redundancy issue via the non-backtracking graph neural network (NBA-GNN) that updates a message without incorporating the message from the previously visited node. We theoretically investigate how NBA-GNN alleviates the over-squashing of GNNs, and establish a connection between NBA-GNN and the impressive performance of non-backtracking updates for stochastic block model recovery. Furthermore, we empirically verify the effectiveness of our NBA-GNN on the long-range graph benchmark and transductive node classification problems.

cs.LG↗

arXiv2020

Regret in Online Recommendation Systems

This paper proposes a theoretical analysis of recommendation systems in an online setting, where items are sequentially recommended to users over time. In each round, a user, randomly picked from a population of $m$ users, requests a recommendation. The decision-maker observes the user and selects an item from a catalogue of $n$ items. Importantly, an item cannot be recommended twice to the same user. The probabilities that a user likes each item are unknown. The performance of the recommendation algorithm is captured through its regret, considering as a reference an Oracle algorithm aware of these probabilities. We investigate various structural assumptions on these probabilities: we derive for each structure regret lower bounds, and devise algorithms achieving these limits. Interestingly, our analysis reveals the relative weights of the different components of regret: the component due to the constraint of not presenting the same item twice to the same user, that due to learning the chances users like items, and finally that arising when learning the underlying structure.

stat.ML↗

arXiv2017

Community Detection with Colored Edges

In this paper, we prove a sharp limit on the community detection problem with colored edges. We assume two equal-sized communities and there are $m$ different types of edges. If two vertices are in the same community, the distribution of edges follows $p_i=α_i\log{n}/n$ for $1\leq i \leq m$, otherwise the distribution of edges is $q_i=β_i\log{n}/n$ for $1\leq i \leq m$, where $α_i$ and $β_i$ are positive constants and $n$ is the total number of vertices. Under these assumptions, a fundamental limit on community detection is characterized using the Hellinger distance between the two distributions. If $\sum_{i=1}^{m} {(\sqrt{α_i} - \sqrt{β_i})}^2 >2$, then the community detection via maximum likelihood (ML) estimator is possible with high probability. If $\sum_{i=1}^m {(\sqrt{α_i} - \sqrt{β_i})}^2 < 2$, the probability that the ML estimator fails to detect the communities does not go to zero.

cs.SI↗