Mining significant graph patterns by leap search

By Xifeng Yan, Hong Cheng, Jiawei Han, Philip S. Yu. SIGMOD Conference, 2008

356 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3c9b329859e33b236dccfb29003da3321b3eebca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

In Signal Processing

Citations in the corpus

Near-optimal Supervised Feature Selection among Frequent Subgraphs (2009, background): “An interesting question for future research is to find optimality guarantees for the horizontal leap search strategy for pattern mining which was recently proposed in [29], or to speed up CORK by employing this search strategy while maintaining its theoretical properties.”
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (2019, methodology): “To show that our kernels also scale to larger datasets, we additionally used the mid-scale Y EAST , Y EAST H, UACC257, UACC257H, OVCAR-8, OVCAR-8H datasets.”
Multi-graph-view Learning for Graph Classification (2014, unknown): “, frequent subgraphs [3] or co-occurrent subgraphs [4]) as features, and then convert each graph into a feature vector, such that generic learning methods can be directly applied for classification.”
Multiple Structure-View Learning for Graph Classification (2018, methodology): “Supervised subgraph feature extraction methods have also been proposed to find discriminative subgraph features for different classes, such as LEAP [20], gPLS [21], COPK [22], and GAIA [23].”
Correlated itemset mining in ROC space: a constraint programming approach (2009, unknown): “The restriction that only |φ(I)| is available during the search is not present in the pruning strategy proposed in [21] and used in [21, 24, 22, 6, 30, 5, 17, 27].”
GAIA: graph classification using evolutionary computation (2010, background): “Leap [Yan, 2008] is a pioneer in discriminative subgraph pattern mining.”
TUDataset: A collection of benchmark datasets for learning with graphs (2020, methodology): “This includes the eleven datasets from anticancer screen tests with different cancer cell lines used by Yan et al. (2008) to demonstrate the efﬁcacy of classiﬁers based on signiﬁcant graph patterns.”
GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph (2014, background): “LEAP [28] and GRAPHSIG [24], on the other hand, discover important subgraphs that are not necessarily frequent.”