70 citations · 467 across the 48 of their papers we have counts for
22 papers · 1 filter
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…
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…
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…
Set2Box: Similarity Preserving Representation Learning of Sets
Geon Lee, Chanyoung Park, Kijung Shin
Sets have been used for modeling various types of objects (e.g., a document as the set of keywords in it and a customer as the set of the items that she has purchased). Measuring s…
Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators
Sunwoo Kim, Minyoung Choe, Jaemin Yoo +1
Group interactions are prevalent in a variety of areas. Many of them, including email exchanges, chemical reactions, and bitcoin transactions, are directional, and thus they are na…
HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams
Geon Lee, Minyoung Choe, Kijung Shin
Sequences of group interactions, such as emails, online discussions, and co-authorships, are ubiquitous; and they are naturally represented as a stream of hyperedges. Despite their…
On the Persistence of Higher-Order Interactions in Real-World Hypergraphs
Hyunjin Choo, Kijung Shin
A hypergraph is a generalization of an ordinary graph, and it naturally represents group interactions as hyperedges (i.e., arbitrary-sized subsets of nodes). Such group interaction…
Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs
Hyeonjeong Shin, Taehyung Kwon, Neil Shah +1
A variety of tasks on dynamic graphs, including anomaly detection, community detection, compression, and graph understanding, have been formulated as problems of identifying consti…
THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting
Geon Lee, Kijung Shin
Group interactions arise in our daily lives (email communications, on-demand ride sharing, comment interactions on online communities, to name a few), and they together form hyperg…
How Do Hyperedges Overlap in Real-World Hypergraphs? -- Patterns, Measures, and Generators
Geon Lee, Minyoung Choe, Kijung Shin
Hypergraphs, a generalization of graphs, naturally represent groupwise relationships among multiple individuals or objects, which are common in many application areas, including we…
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
Minji Yoon, Bryan Hooi, Kijung Shin +1
Given a dynamic graph stream, how can we detect the sudden appearance of anomalous patterns, such as link spam, follower boosting, or denial of service attacks? Additionally, can w…
Summarizing graphs using the configuration model
Houquan Zhou, Shenghua Liu, Kyuhan Lee +3
Given a large graph, how can we summarize it with fewer nodes and edges while maintaining its key properties, such as spectral property? Although graphs play more and more importan…
Evolution of Real-world Hypergraphs: Patterns and Models without Oracles
Yunbum Kook, Jihoon Ko, Kijung Shin
What kind of macroscopic structural and dynamical patterns can we observe in real-world hypergraphs? What can be underlying local dynamics on individuals, which ultimately lead to…
Structural Patterns and Generative Models of Real-world Hypergraphs
Manh Tuan Do, Se-eun Yoon, Bryan Hooi +1
Graphs have been utilized as a powerful tool to model pairwise relationships between people or objects. Such structure is a special type of a broader concept referred to as hypergr…
How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction
Se-eun Yoon, Hyungseok Song, Kijung Shin +1
Hypergraphs provide a natural way of representing group relations, whose complexity motivates an extensive array of prior work to adopt some form of abstraction and simplification…
MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks
Jihoon Ko, Kyuhan Lee, Kijung Shin +1
Influence maximization (IM) is one of the most important problems in social network analysis. Its objective is to find a given number of seed nodes that maximize the spread of info…
DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams
Kijung Shin, Bryan Hooi, Jisu Kim +1
Consider a stream of retweet events - how can we spot fraudulent lock-step behavior in such multi-aspect data (i.e., tensors) evolving over time? Can we detect it in real time, wit…