Graph can conveniently represent the relation between things in the real world, but the categories of things and the relation between things are various. recv ( recv_func, msg ) Highlight: Flexibility - Natively Support Heterogeneous Graph Learning tensor () def send_func ( src_feat, dst_feat, edge_feat ): return src_feat def recv_func ( msg ): return msg. Graph ( num_nodes = num_nodes, edges = edges, node_feat = ) g. import pgl import paddle import numpy as np num_nodes = 5 edges = feature = np. To write a sum aggregator, users only need to write the following codes. For the second step, the recv function is responsible for aggregating messages together from different sources. As shown in the following figure, for the first step the send function is defined on the edges of the graph, and the user can customize the send function to send the message from the source to the target node. Users only need to write send and recv functions to easily implement a simple GCN. At PGL we adopt Message Passing Paradigm similar to DGL to help to build a customize graph neural network easily. One of the most important benefits of graph neural networks compared to other models is the ability to use node-to-node connectivity information, but coding the communication between nodes is very cumbersome. Combined with the PaddlePaddle deep learning framework, we are able to support both graph representation learning models and graph neural networks, and thus our framework has a wide range of graph-based applications. Furthermor, The newly released PGL also support distributed graph storage and some distributed training algorithms, such as distributed deep walk and distributed graphsage. The newly released PGL supports heterogeneous graph learning on both walk based paradigm and message-passing based paradigm by providing MetaPath sampling and Message Passing mechanism on heterogeneous graph. Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle.
Our Dstributed Graph Engine API has been released and we developed a tutorial to show how to launch a graph engine and a demo for training model using graph engine.PGL Dstributed Graph Engine API released!! HGAMN: Heterogeneous Graph Attention Matching Network for Multilingual POI Retrieval at Baidu Maps, to appear in KDD2021.Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification, to appear in IJCAI2021.Two amazing paper using PGL are accepted: (2021.06.17)
Second place in PCQM4M-LSC track: Code and Technical Report can be found here. Super excited to announce our PGL team won TWO FIRST place and ONE SECOND place in a total of three track in OGB-LSC KDD CUP 2021.įirst place in MAG240M-LSC track: Code and Technical Report can be found here.įirst place in WikiKG90M-LSC track: Code and Technical Report can be found here. GNNAutoScale: PGL now supports GNNAutoScale framework, which can scale arbitrary message-passing GNNs to large graphs. Graph4KG: We released a flexible framework named Graph4KG to learn embeddings of entities and relations in KGs, which supports training on massive KGs. Requirements: Intel, 64-bit processor, OS X 10.10 or later.Graph4Rec: We released a universal and large-scale toolkit with graph neural networks for recommender systems. Fixed the font drag & drop issue in family group view.
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