# Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
## 5 Experiments  
### Dataset and Evaluation Metrics  
We evaluate our HGPN model on four real-world heterogeneous graph datasets, ranging from different scales: ACM, DBLP, IMDB and OGB-MAG [10]. We list the statistics of these datasets in Table 1.  
**DBLP:** a subset of the DBLP computer science bibliography, comprising 14,328 papers, 4057 authors, 20 venues, and 8789 terms. The initial attributes of paper nodes are bag-of-words vectors of their keywords, while the initial attributes of author nodes are bag-of-words representations of their affiliates, titles, and keywords extracted from their published papers. For the attributes of terms, no computer science specialized pre-trained word vectors were used, while the attributes of venues were represented using one-hot vectors. The authors in the dataset are the target nodes and are grouped into four research areas based on the conferences to which they submitted.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Nodes** & **Node Types** & **Edges** & **Edge Types** & **Classes** & **Target** \\ \hline ACM & 10,942 & 4 & 547,842 & 8 & 3 & paper \\ DBLP & 26,128 & 4 & 239,566 & 6 & 4 & author \\ IMDB & 21,420 & 4 & 86,642 & 6 & 5 & movie \\ OGB-MAG & 1,939,743 & 4 & 42,222,014 & 8 & 349 & paper \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of datasets used in this paper.  
**ACM:** a subset extracted from ACM citation network, which comprises 4019 papers, 7167 authors and 60 subjects. The attributes of the papers are bag-of-words representations of their keywords, and the attributes of the authors are bag-of-words representations of affiliates, titles and keywords extracted from their published papers. For subjects, their attributes are bag-of-words representations of keywords from their connected papers. The target nodes are papers, which are divided into three classes according to the conference at which they were published.  
**IMDB:** a benchmark dataset in movie recommendation, which includes 4080 movies, 5075 actors and 2001 directors. We collected a subset of IMDB in five subjects of movies: action, comedy, romance, drama and thriller. The target node is the movie node and the label is the subject of movie. The movie's attribute is a bag-of words vector represented by plots.  
**OGB-MAG:** a heterogeneous academic network extracted from the Microsoft Academic Graph (MAG), which includes four types of nodes: papers (P), authors (A), fields (F), and institutions (I). The network comprises four types of directed edges: an author is \"affiliated with\" an institution, an author \"writes\" a paper, a paper \"cites\" a paper, and a paper \"has a topic of\" a field of study. To account for bidirectional relationships, we include related reverse edges for all directed edges. The papers in the network are published across 349 different venues, and the task is to predict the venue for each paper. Each paper node has an associated Word2 Vec [14] attribute. We use the metapath2 vec [17] model to generate features for other types of nodes that do not have input features.  
For all four datasets, the task is node classification. Consistent with previous studies, we evaluate the ACM, DBLP, and IMDB datasets using the Macro-F1 and Micro-F1 metrics, while the OGB-MAG dataset is evaluated using the accuracy metric.