Demo Data from EDBT 2026 / reviewers in the wild / expert
Xingming Liu
dblp:47/1892
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Computer networks · 1
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.
| Computer networks
1 paper |
Routing and switching · 91% Network optimization and economics · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Routing and switching › routing algorithms
heuristic routing algorithm |
0.0 | 1 | 2001 | Heuristic Algorithms for Multi-Constrained Quality of Service Routing · INFOCOM 2001 |
Routing and switching › qos routing
multi-constrained routing |
0.0 | 1 | 2001 | Heuristic Algorithms for Multi-Constrained Quality of Service Routing · INFOCOM 2001 |
Routing and switching
qos routing |
0.0 | 1 | 2001 | Heuristic Algorithms for Multi-Constrained Quality of Service Routing · INFOCOM 2001 |
Network optimization and economics
resource allocation |
0.0 | 1 | 2001 | Heuristic Algorithms for Multi-Constrained Quality of Service Routing · INFOCOM 2001 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.0limited path heuristic · 0.0limited granularity heuristic · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Multi-label feature selection using adaptive heterogeneous graph-based learning and self-adaptive evolutionary optimization with local refinement
Xingming Liu, Ning Pan |
Eng. Appl. Artif. Intell. | 1 |
| 2001 | Heuristic Algorithms for Multi-Constrained Quality of Service RoutingabstractMulti-constrained quality of service (QoS) routing finds a route in the network that satisfies multiple independent quality of service constraints. This problem is NP-hard and a number of heuristic algorithms have been proposed to solve the problem. This paper studies two heuristics, the limited granularity heuristic and the limited path heuristic, for solving general k-constrained problems. Analytical and simulation studies are conducted to compare the time/space requirements of the heuristics and the effectiveness of the heuristics in finding the paths that satisfy the QoS constraints. We prove analytically that for an N nodes and E edges network with k (a small constant) independent QoS constraints, the limited granularity heuristic must maintain a table of size O(|N|/sup k-1/) in each node to be effective, which results in a time complexity of O(|N|/sup k/|E|). We also prove that the limited path heuristic can achieve very high performance by maintaining O(|N|/sup 2/lg(|N|)) entries in each node, which indicates that the performance of the limited path heuristic is not sensitive to the number of constraints. We conclude that although both the limited granularity heuristic and the limited path heuristic can efficiently solve 2-constrained QoS routing problems, the limited path heuristic is superior to the limited granularity heuristic in solving k-constrained QoS routing problems when k>3. Our simulation study further confirms this conclusion. Xingming Liu |
INFOCOM | 2 |