70 citations · 467 across the 48 of their papers we have counts for
12 papers · 1 filter
Robust Graph Clustering via Meta Weighting for Noisy Graphs
Hyeonsoo Jo, Fanchen Bu, Kijung Shin
How can we find meaningful clusters in a graph robustly against noise edges? Graph clustering (i.e., dividing nodes into groups of similar ones) is a fundamental problem in graph a…
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs
Dongjin Lee, Juho Lee, Kijung Shin
Real-world graphs are dynamic, constantly evolving with new interactions, such as financial transactions in financial networks. Temporal Graph Neural Networks (TGNNs) have been dev…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
On Improving the Cohesiveness of Graphs by Merging Nodes: Formulation, Analysis, and Algorithms
Fanchen Bu, Kijung Shin
Graphs are a powerful mathematical model, and they are used to represent real-world structures in various fields. In many applications, real-world structures with high connectivity…
Classification of Edge-dependent Labels of Nodes in Hypergraphs
Minyoung Choe, Sunwoo Kim, Jaemin Yoo +1
A hypergraph is a data structure composed of nodes and hyperedges, where each hyperedge is an any-sized subset of nodes. Due to the flexibility in hyperedge size, hypergraphs repre…
Towards Deep Attention in Graph Neural Networks: Problems and Remedies
Soo Yong Lee, Fanchen Bu, Jaemin Yoo +1
Graph neural networks (GNNs) learn the representation of graph-structured data, and their expressiveness can be further enhanced by inferring node relations for propagation. Attent…
How Transitive Are Real-World Group Interactions? -- Measurement and Reproduction
Sunwoo Kim, Fanchen Bu, Minyoung Choe +2
Many real-world interactions (e.g., researcher collaborations and email communication) occur among multiple entities. These group interactions are naturally modeled as hypergraphs.…
Interplay between Topology and Edge Weights in Real-World Graphs: Concepts, Patterns, and an Algorithm
Fanchen Bu, Shinhwan Kang, Kijung Shin
What are the relations between the edge weights and the topology in real-world graphs? Given only the topology of a graph, how can we assign realistic weights to its edges based on…
NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors
Taehyung Kwon, Jihoon Ko, Jinhong Jung +1
Many real-world data are naturally represented as a sparse reorderable matrix, whose rows and columns can be arbitrarily ordered (e.g., the adjacency matrix of a bipartite graph).…
Characterization of Simplicial Complexes by Counting Simplets Beyond Four Nodes
Hyunju Kim, Jihoon Ko, Fanchen Bu +1
Simplicial complexes are higher-order combinatorial structures which have been used to represent real-world complex systems. In this paper, we concentrate on the local patterns in…
Graphlets over Time: A New Lens for Temporal Network Analysis
Deukryeol Yoon, Dongjin Lee, Minyoung Choe +1
Graphs are widely used for modeling various types of interactions, such as email communications and online discussions. Many of such real-world graphs are temporal, and specificall…
Hypercore Decomposition for Non-Fragile Hyperedges: Concepts, Algorithms, Observations, and Applications
Fanchen Bu, Geon Lee, Kijung Shin
Hypergraphs are a powerful abstraction for modeling high-order relations, which are ubiquitous in many fields. A hypergraph consists of nodes and hyperedges (i.e., subsets of nodes…