Demo Data from EDBT 2026 / reviewers in the wild / expert
Hangyu Xu
dblp:253/8250
· DBLP profile ↗
3ranked-venue papers
0as first author
3since 2021 · last 2026
0000-0002-8204-161XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Privacy and data protection · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining › structured data mining › graph mining
network motif |
0.9 | 1 | 2025 | Differentially Private Synthetic Graphs Preserving Triangle-Motif Cuts · COLT 2025 |
Privacy and data protection
differential privacy |
0.9 | 1 | 2025 | Differentially Private Synthetic Graphs Preserving Triangle-Motif Cuts · COLT 2025 |
Privacy and data protection › data publishing › privacy-preserving data publishing
synthetic graph generation |
0.9 | 1 | 2025 | Differentially Private Synthetic Graphs Preserving Triangle-Motif Cuts · COLT 2025 |
Methods — techniques the papers use, named apart from their topics
sensitivity analysis · 1.7differential privacy mechanism · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Lower Bounds on Tree CoversabstractGiven an n-point metric space (X,d_X), a tree cover 𝒯 is a set of |𝒯| = k trees on X such that every pair of vertices in X has a low-distortion path in one of the trees in 𝒯. Tree covers have been playing a crucial role in graph algorithms for decades, and the research focus is the construction of tree covers with small size k and distortion. When k = 1, the best distortion is known to be Θ(n). For a constant k ≥ 2, the best distortion upper bound is Õ(n^{1/k}) and the strongest lower bound is Ω(log_k n), leaving a gap to be closed. In this paper, we improve the lower bound to Ω(n^{1/(2^{k-1)}}). Our proof is a novel analysis on a structurally simple grid-like graph, which utilizes some combinatorial fixed-point theorems. We believe that they will prove useful for analyzing other tree-like data structures as well. Yu Chen 0039, Zihan Tan, Hangyu Xu |
ITCS | 3 |
| 2026 | Lower Bounds on Tree CoversabstractAbstract. Given an [Formula: see text]-point metric space [Formula: see text], a tree cover [Formula: see text] is a set of [Formula: see text] trees on [Formula: see text] such that every pair of vertices in [Formula: see text] has a low-distortion path in one of the trees in [Formula: see text]. Tree covers have been playing a crucial role in graph algorithms for decades, and the research focus is the construction of tree covers with small size [Formula: see text] and distortion. When [Formula: see text], the best distortion is known to be [Formula: see text]. For a constant [Formula: see text], the best distortion upper bound is [Formula: see text] and the strongest lower bound is [Formula: see text], leaving a gap to be closed. In this paper, we improve the lower bound to [Formula: see text]. Our proof is a novel analysis on a structurally simple grid-like graph, which utilizes some combinatorial fixed-point theorems. We believe that they will prove useful for analyzing other tree-like data structures as well. Zihan Tan, Hangyu Xu |
SIAM J. Comput. | 3 |
| 2025 | Differentially Private Synthetic Graphs Preserving Triangle-Motif CutsabstractWe study the problem of releasing a differentially private (DP) synthetic graph $G’$ that well approximates the triangle-motif sizes of all cuts of any given graph $G$, where a motif in general refers to a frequently occurring subgraph within complex networks. Non-private versions of such graphs have found applications in diverse fields such as graph clustering, graph sparsification, and social network analysis. Specifically, we present the first $(\varepsilon,\delta)$-DP mechanism that, given an input graph $G$ with $n$ vertices, $m$ edges and local sensitivity of triangles $\ell_{3}(G)$, generates a synthetic graph $G’$ in polynomial time, approximating the triangle-motif sizes of all cuts $(S,V\setminus S)$ of the input graph $G$ up to an additive error of $\tilde{O}(\sqrt{m\ell_3(G)}n/\varepsilon^{3/2})$. Additionally, we provide a lower bound of $\Omega(\sqrt{mn}\ell_3(G)/\varepsilon)$ on the additive error for any DP algorithm that answers the triangle-motif size queries of all $(S,T)$-cut of $G$. Finally, our algorithm generalizes to weighted graphs, and our lower bound extends to any $K_h$-motif cut for any constant $h\geq 2$. Pan Peng 0001, Hangyu Xu |
COLT | 2 |