Name
Abstract | ||
|---|---|---|
Graph mining algorithms that seek to find interesting structure in a graph are compelling for many reasons but may not lead to useful information learned from the data. This position paper explores the current graph mining approaches and suggests why certain algorithms may provide misleading information whereas others may be just what is needed. In particular, algorithms that ignore the rich set of node and edge properties that are prevalent in many real-world graphs are in danger of finding results based on the wrong information. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IPDPSW.2013.44 | IPDPS Workshops |
Keywords | Field | DocType |
data mining,graph theory,learning (artificial intelligence),edge properties,graph mining algorithm,graph structure,graph-based learning,misleading information,node properties,property values,structure values,Graph Mining,Position Paper | Graph theory,Graph,Graph database,Computer science,Position paper,Theoretical computer science,Artificial intelligence,Graph (abstract data type),Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David J. Haglin | 1 | 112 | 19.45 |
| Lawrence B. Holder | 2 | 1448 | 259.29 |