Learning metrics for persistence-based summaries and applications for graph classification

arXiv:1904.12189 2019 Qi ZhaoYusu Wang

Recently a new feature representation and data analysis methodology based on a topological tool called persistent homology (and its corresponding persistence diagram summary) has started to attract momentum. A series of methods have been developed to map a persistence diagram to a vector representation so as to facilitate the downstream use of machine learning tools, and in these approaches, the importance (weight) of different persistence features are often preset... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Graph Classification D&D WKPI-kmeans Accuracy 82.0% # 3
Graph Classification IMDb-B WKPI-kcenters Accuracy 75.4% # 5
Graph Classification IMDb-M WKPI-kcenters Accuracy 49.5% # 12
Graph Classification MUTAG WKPI-kcenters Accuracy 87.5% # 19
Graph Classification NCI1 WKPI-kmeans Accuracy 87.2% # 1
Graph Classification NCI109 WKPI-kcenters Accuracy 87.3 # 1
Graph Classification NEURON-Average WKPI-kmeans Accuracy 73.50 # 2
Graph Classification NEURON-Average WKPI-kcenters Accuracy 77.80 # 1
Graph Classification NEURON-BINARY WKPI-kcenters Accuracy 86.5 # 2
Graph Classification NEURON-BINARY WKPI-kmeans Accuracy 90.3 # 1
Graph Classification NEURON-MULTI WKPI-kmeans Accuracy 56.2 # 3
Graph Classification NEURON-MULTI WKPI-kcenters Accuracy 69.1 # 1
Graph Classification PROTEINS WKPI-kmeans Accuracy 78.8% # 2