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Title: Geom-GCN: Geometric Graph Convolutional Networks

Paper content:
Message-passing neural networks (MPNNs) have been successfully applied in a wide variety of applications in the real world.
However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs.
Few studies have noticed the weaknesses from different perspectives.
From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses.
The behind basic idea is the aggregation on a graph can benefit from a continuous space underlying the graph.
The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation.
We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs.
Experimental results show the proposed Geom-GCN achieved state-of-the-art performance on a wide range of open datasets of graphs.
Message-passing neural networks (MPNNs), such as GNN (Scarselli et al., 2008) , ChebNet (Defferrard et al., 2016) , GG-NN (Li et al., 2016) , GCN (Kipf & Welling, 2017) , are powerful for learning on graphs with various applications ranging from brain networks to online social network (Gilmer et al., 2017; Wang et al., 2019) .
In a layer of MPNNs, each node sends its feature representation, a "message", to the nodes in its neighborhood; and then updates its feature representation by aggregating all "messages" received from the neighborhood.
The neighborhood is often defined as the set of adjacent nodes in graph.
By adopting permutation-invariant aggregation functions (e.g., summation, maximum, and mean), MPNNs are able to learn representations which are invariant to isomorphic graphs, i.e., graphs that are topologically identical.
Although existing MPNNs have been successfully applied in a wide variety of scenarios, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data.
Firstly, the aggregators lose the structural information of nodes in neighborhoods.
Permutation invariance is an essential requirement for any graph learning method.
To meet it, existing MPNNs adopt permutation-invariant aggregation functions which treat all "messages" from neighborhood as a set.
For instance, GCN simply sums the normalized "messages" from all one-hop neighbors (Kipf & Welling, 2017) .
Such aggregation loses the structural information of nodes in neighborhood because it does not distinguish the "messages" from different nodes.
Therefore, after such aggregation, we cannot know which node contributes what to the final aggregated output.
Without modeling such structural information, as shown in (Kondor et al., 2018) and , the existing MPNNs cannot discriminate between certain non-isomorphic graphs.
In those cases, MPNNs may map non-isomorphic graphs to the same feature representations, which is obviously not desirable for graph representation learning.
Unlike MPNNs, classical convolutional neural networks (CNNs) avoid this problem by using aggregators (i.e., convolutional filters) with a structural receiving filed defined on grids, i.e., a Euclidean space, and are hence able to distinguish each input unit.
As shown by our experiments, such structural information often contains clues regarding topology patterns in graph (e.g., hierarchy), and should be extracted and used to learn more discriminating representations for graph-structured data.
Secondly, the aggregators lack the ability to capture long-range dependencies in disassortative graphs.
In MPNNs, the neighborhood is defined as the set of all neighbors one hop away (e.g., GCN), or all neighbors up to r hops away (e.g., ChebNet).
In other words, only messages from nearby nodes are aggregated.
The MPNNs with such aggregation are inclined to learn similar representations for proximal nodes in a graph.
This implies that they are probably desirable methods for assortative graphs (e.g., citation networks (Kipf & Welling, 2017) and community networks ) where node homophily holds (i.e., similar nodes are more likely to be proximal, and vice versa), but may be inappropriate to the disassortative graphs (Newman, 2002) where node homophily does not hold.
For example, Ribeiro et al. (2017) shows disassortative graphs where nodes of the same class exhibit high structural similarity but are far apart from each other.
In such cases, the representation ability of MPNNs may be limited significantly, since they cannot capture the important features from distant but informative nodes.
A straightforward strategy to address this limitation is to use a multi-layered architecture so as to receive "messages" from distant nodes.
For instance, due to the localized nature of convolutional filters in classical CNNs, a single convolutional layer is similarly limited in its representational ability.
CNNs typically use multiple layers connected in a hierarchical manner to learn complex and global representations.
However, unlike CNNs, it is difficult for multi-layer MPNNs to learn good representations for disassortative graphs because of two reasons.
On one hand, relevant messages from distant nodes are mixed indistinguishably with a large number of irrelevant messages from proximal nodes in multi-layer MPNNs, which implies that the relevant information will be "washed out" and cannot be extracted effectively.
On the other hand, the representations of different nodes would become very similar in multi-layer MPNNs, and every node's representation actually carries the information about the entire graph .
In this paper, we overcome the aforementioned weaknesses of graph neural networks starting from two basic observations:
i) Classical neural networks effectively address the similar limitations thanks to the stationarity, locality, and compositionality in a continuous space ;
ii) The notion of network geometry bridges the gap between continuous space and graph (Hoff et al., 2002; Muscoloni et al., 2017) .
Network geometry aims to understand networks by revealing the latent continuous space underlying them, which assumes that nodes are sampled discretely from a latent continuous space and edges are established according to their distance.
In the latent space, complicated topology patterns in graphs can be preserved and presented as intuitive geometry, such as subgraph (Narayanan et al., 2016 ), community (Ni et al., 2019 , and hierarchy (Nickel & Kiela, 2017; .
Inspired by those two observations, we raise an enlightening question about the aggregation scheme in graph neural network.
• Can the aggregation on a graph benefit from a continuous latent space, such as using geometry in the space to build structural neighborhoods and capture long-range dependencies in the graph?
To answer the above question, we propose a novel aggregation scheme for graph neural networks, termed the geometric aggregation scheme.
In the scheme, we map a graph to a continuous latent space via node embedding, and then use the geometric relationships defined in the latent space to build structural neighborhoods for aggregation.
Also, we design a bi-level aggregator operating on the structural neighborhoods to update the feature representations of nodes in graph neural networks, which are able to guarantee permutation invariance for graph-structured data.
Compared with exist-ing MPNNs, the scheme extracts more structural information of the graph and can aggregate feature representations from distant nodes via mapping them to neighborhoods defined in the latent space.
We then present an implementation of the geometric aggregation scheme in graph convolutional networks, which we call Geom-GCN, to perform transductive learning, node classification, on graphs.
We design particular geometric relationships to build the structural neighborhood in Euclidean and hyperbolic embedding space respectively.
We choose different embedding methods to map the graph to a suitable latent space for different applications, where suitable topology patterns of graph are preserved.
Finally, we empirically validate and analyze Geom-GCN on a wide range of open datasets of graphs, and Geom-GCN achieved the state-of-the-art results.
In summary, the contribution of this paper is three-fold:
i) We propose a novel geometric aggregation scheme for graph neural network, which operates in both graph and latent space, to overcome the aforementioned two weaknesses;
ii) We present an implementation of the scheme, Geom-GCN, for transductive learning in graph;
iii) We validate and analyze Geom-GCN via extensive comparisons with state-of-the-art methods on several challenging benchmarks.
We tackle the two major weaknesses of existing message-passing neural networks over graphslosses of discriminative structures and long-range dependencies.
As our key insight, we bridge a discrete graph to a continuous geometric space via graph embedding.
That is, we exploit the principle of convolution: spatial aggregation over a meaningful space-and our approach thus extracts or "recovers" the lost information (discriminative structures and long-range dependencies) in an embedding space from a graph.
We proposed a general geometric aggregation scheme and instantiated it with several specific Geom-GCN implementations, and our experiments validated clear advantages over the state-of-the-art.
As future work, we will explore techniques for choosing a right embedding method-depending not only on input graphs but also on target applications, such as epidemic dynamic prediction on social contact network (Yang et al., 2017; Pei et al., 2018) .

assistant: text: For graph neural networks, the aggregation on a graph can benefit from a continuous space underlying the graph.