Industrial purposes of machine studying are generally composed of varied objects which have differing knowledge modalities or characteristic distributions. Heterogeneous graphs (HGs) supply a unified view of those multimodal knowledge programs by defining a number of varieties of nodes (for every knowledge sort) and edges (for the relation between knowledge objects). As an example, e-commerce networks may need [user, product, review] nodes or video platforms may need [channel, user, video, comment] nodes. Heterogeneous graph neural networks (HGNNs) study node embeddings summarizing every node’s relationships right into a vector. Nevertheless, in actual world HGs, there may be usually a label imbalance subject between completely different node sorts. Which means that label-scarce node sorts can’t exploit HGNNs, which hampers the broader applicability of HGNNs.
In “Zero-shot Switch Studying inside a Heterogeneous Graph by way of Information Switch Networks”, offered at NeurIPS 2022, we suggest a mannequin referred to as a Information Switch Community (KTN), which transfers information from label-abundant node sorts to zero-labeled node sorts utilizing the wealthy relational data given in a HG. We describe how we pre-train a HGNN mannequin with out the necessity for fine-tuning. KTNs outperform state-of-the-art switch studying baselines by as much as 140% on zero-shot studying duties, and can be utilized to enhance many present HGNN fashions on these duties by 24% (or extra).
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| KTNs remodel labels from one sort of knowledge (squares) by way of a graph to a different sort (stars). |
What’s a heterogeneous graph?
A HG consists of a number of node and edge sorts. The determine beneath reveals an e-commerce community offered as a HG. In e-commerce, “customers” buy “merchandise” and write “opinions”. A HG presents this ecosystem utilizing three node sorts [user, product, review] and three edge sorts [user-buy-product, user-write-review, review-on-product]. Particular person merchandise, customers, and opinions are then offered as nodes and their relationships as edges within the HG with the corresponding node and edge sorts.
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| E-commerce heterogeneous graph. |
Along with all connectivity data, HGs are generally given with enter node attributes that summarize every node’s data. Enter node attributes might have completely different modalities throughout completely different node sorts. As an example, pictures of merchandise might be given as enter node attributes for the product nodes, whereas textual content may be given as enter attributes to evaluate nodes. Node labels (e.g., the class of every product or the class that the majority pursuits every consumer) are what we need to predict on every node.
HGNNs and label shortage points
HGNNs compute node embeddings that summarize every node’s native constructions (together with the node and its neighbor’s data). These node embeddings are utilized by a classifier to foretell every node’s label. To coach a HGNN mannequin and a classifier to foretell labels for a particular node sort, we require quantity of labels for the sort.
A standard subject in industrial purposes of deep studying is label shortage, and with their various node sorts, HGNNs are much more prone to face this problem. As an example, publicly obtainable content material node sorts (e.g., product nodes) are abundantly labeled, whereas labels for consumer or account nodes is probably not obtainable resulting from privateness restrictions. Which means that in most traditional coaching settings, HGNN fashions can solely study to make good inferences for a number of label-abundant node sorts and may normally not make any inferences for any remaining node sorts (given the absence of any labels for them).
Switch studying on heterogeneous graphs
Zero-shot switch studying is a way used to enhance the efficiency of a mannequin on a goal area with no labels through the use of the information realized by the mannequin from one other associated supply area with adequately labeled knowledge. To use switch studying to unravel this label shortage subject for sure node sorts in HGs, the goal area could be the zero-labeled node sorts. Then what could be the supply area? Earlier work generally units the supply area as the identical sort of nodes situated in a special HG, assuming these nodes are abundantly labeled. This graph-to-graph switch studying strategy pre-trains a HGNN mannequin on the exterior HG after which runs the mannequin on the unique (label-scarce) HG.
Nevertheless, these approaches will not be relevant in lots of real-world situations for 3 causes. First, any exterior HG that might be utilized in a graph-to-graph switch studying setting would virtually absolutely be proprietary, thus, probably unavailable. Second, even when practitioners might get hold of entry to an exterior HG, it’s unlikely the distribution of that supply HG would match their goal HG effectively sufficient to use switch studying. Lastly, node sorts affected by label shortage are prone to undergo the identical subject on different HGs (e.g., privateness points on consumer nodes).
Our strategy: Switch studying between node sorts inside a heterogeneous graph
Right here, we make clear a extra sensible supply area, different node sorts with plentiful labels situated on the identical HG. As a substitute of utilizing further HGs, we switch information inside a single HG (assumed to be totally owned by the practitioners) throughout various kinds of nodes. Extra particularly, we pre-train a HGNN mannequin and a classifier on a label-abundant (supply) node sort, then reuse the fashions on the zero-labeled (goal) node sorts situated in the identical HG with out extra fine-tuning. The one requirement is that the supply and goal node sorts share the identical label set (e.g., within the e-commerce HG, product nodes have a label set describing product classes, and consumer nodes share the identical label set describing their favourite purchasing classes).
Why is it difficult?
Sadly, we can’t instantly reuse the pre-trained HGNN and classifier on the goal node sort. One essential attribute of HGNN architectures is that they’re composed of modules specialised to every node sort to totally study the multiplicity of HGs. HGNNs use distinct units of modules to compute embeddings for every node sort. Within the determine beneath, blue- and red-colored modules are used to compute node embeddings for the supply and goal node sorts, respectively.
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| HGNNs are composed of modules specialised to every node sort and use distinct units of modules to compute embeddings of various node sorts. Extra particulars may be discovered within the paper. |
Whereas pre-training HGNNs on the supply node sort, source-specific modules within the HGNNs are effectively educated, nonetheless target-specific modules are under-trained as they’ve solely a small quantity of gradients flowing into them. That is proven beneath, the place we see that the L2 norm of gradients for goal node sorts (i.e., Mtt) are a lot decrease than for supply sorts (i.e., Mss). On this case a HGNN mannequin outputs poor node embeddings for the goal node sort, which ends up in poor job efficiency.
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| In HGNNs, goal type-specific modules obtain zero or solely a small quantity of gradients throughout pre-training on the supply node sort, resulting in poor efficiency on the goal node sort. |
KTN: Trainable cross-type switch studying for HGNNs
Our work focuses on remodeling the (poor) goal node embeddings computed by a pre-trained HGNN mannequin to comply with the distribution of the supply node embeddings. Then the classifier, pre-trained on the supply node sort, may be reused for the goal node sort. How can we map the goal node embeddings to the supply area? To reply this query, we examine how HGNNs compute node embeddings to study the connection between supply and goal distributions.
HGNNs combination linked node embeddings to reinforce a goal node’s embeddings in every layer. In different phrases, the node embeddings for each supply and goal node sorts are up to date utilizing the identical enter — the earlier layer’s node embeddings of any linked node sorts. Which means that they are often represented by one another. We show this relationship theoretically and discover there’s a mapping matrix (outlined by HGNN parameters) from the goal area to the supply area (extra particulars in Theorem 1 within the paper). Primarily based on this theorem, we introduce an auxiliary neural community, which we check with as a Information Switch Community (KTN), that receives the goal node embeddings after which transforms them by multiplying them with a (trainable) mapping matrix. We then outline a regularizer that’s minimized together with the efficiency loss within the pre-training section to coach the KTN. At take a look at time, we map the goal embeddings computed from the pre-trained HGNN to the supply area utilizing the educated KTN for classification.
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| In HGNNs, the ultimate node embeddings of each supply and goal sorts are computed from completely different mathematical capabilities (f(): supply, g(): goal) which use the identical enter — the earlier layer’s node embeddings. |
Experimental outcomes
To look at the effectiveness of KTNs, we ran 18 completely different zero-shot switch studying duties on two public heterogeneous graphs, Open Educational Graph and Pubmed. We evaluate KTN with eight state-of-the-art switch studying strategies (DAN, JAN, DANN, CDAN, CDAN-E, WDGRL, LP, EP). Proven beneath, KTN persistently outperforms all baselines on all duties, beating switch studying baselines by as much as 140% (as measured by Normalized Discounted Cumulative Achieve, a rating metric).
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| Zero-shot switch studying on Open Educational Graph (OAG-CS) and Pubmed datasets. The colours signify completely different classes of switch studying baselines towards which the outcomes are in contrast. Yellow: Use statistical properties (e.g., imply, variance) of distributions. Inexperienced: Use adversarial fashions to switch information. Orange: Switch information instantly by way of graph construction utilizing label propagation. |
Most significantly, KTN may be utilized to virtually all HGNN fashions which have node and edge type-specific parameters and enhance their zero-shot efficiency on course domains. As proven beneath, KTN improves accuracy on zero-labeled node sorts throughout six completely different HGNN fashions(R-GCN, HAN, HGT, MAGNN, MPNN, H-MPNN) by as much as 190%.
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| KTN may be utilized to 6 completely different HGNN fashions and enhance their zero-shot efficiency on course domains. |
Takeaways
Numerous ecosystems in business may be offered as heterogeneous graphs. HGNNs summarize heterogeneous graph data into efficient representations. Nevertheless, label shortage points on sure varieties of nodes forestall the broader utility of HGNNs. On this put up, we launched KTN, the primary cross-type switch studying methodology designed for HGNNs. With KTN, we are able to totally exploit the richness of heterogeneous graphs by way of HGNNs no matter label shortage. See the paper for extra particulars.
Acknowledgements
This paper is joint work with our co-authors John Palowitch (Google Analysis), Dustin Zelle (Google Analysis), Ziniu Hu (Intern, Google Analysis), and Russ Salakhutdinov (CMU). We thank Tom Small for creating the animated determine on this weblog put up.
