Travelled to:1 × Australia
1 × China
1 × Spain
4 × USA
Collaborated with:T.Eliassi-Rad C.Faloutsos H.Tong K.Henderson D.Jensen J.Neville D.Koutra L.Akoglu L.Li N.Shah T.Zou S.Basu
Talks about:graph (4) structur (2) classif (2) mine (2) larg (2) use (2) nonparametr (1) interpret (1) distribut (1) attribut (1)
Person: Brian Gallagher
DBLP: Gallagher:Brian
Contributed to:
2015
201520122011200820072004Wrote 7 papers:
- KDD-2015-ShahKZGF #graph #named #summary
- TimeCrunch: Interpretable Dynamic Graph Summarization (NS, DK, TZ, BG, CF), pp. 1055–1064.
- SAC-2015-HendersonGE #clustering #empirical #named #parametricity #performance #probability
- EP-MEANS: an efficient nonparametric clustering of empirical probability distributions (KH, BG, TER), pp. 893–900.
- KDD-2012-HendersonGETBAKFL #graph #mining #named #scalability
- RolX: structural role extraction & mining in large graphs (KH, BG, TER, HT, SB, LA, DK, CF, LL), pp. 1231–1239.
- KDD-2011-HendersonGLAETF #graph #mining #recursion #using
- It’s who you know: graph mining using recursive structural features (KH, BG, LL, LA, TER, HT, CF), pp. 663–671.
- KDD-2008-GallagherTEF #classification #network #using
- Using ghost edges for classification in sparsely labeled networks (BG, HT, TER, CF), pp. 256–264.
- KDD-2007-TongFGE #graph #pattern matching #performance #scalability
- Fast best-effort pattern matching in large attributed graphs (HT, CF, BG, TER), pp. 737–746.
- KDD-2004-JensenNG #classification #relational #why
- Why collective inference improves relational classification (DJ, JN, BG), pp. 593–598.