2 research outputs found
Review of Extreme Multilabel Classification
Extreme multilabel classification or XML, is an active area of interest in
machine learning. Compared to traditional multilabel classification, here the
number of labels is extremely large, hence, the name extreme multilabel
classification. Using classical one versus all classification wont scale in
this case due to large number of labels, same is true for any other
classifiers. Embedding of labels as well as features into smaller label space
is an essential first step. Moreover, other issues include existence of head
and tail labels, where tail labels are labels which exist in relatively smaller
number of given samples. The existence of tail labels creates issues during
embedding. This area has invited application of wide range of approaches
ranging from bit compression motivated from compressed sensing, tree based
embeddings, deep learning based latent space embedding including using
attention weights, linear algebra based embeddings such as SVD, clustering,
hashing, to name a few. The community has come up with a useful set of metrics
to identify correctly the prediction for head or tail labels.Comment: 46 pages, 13 figure
Improving Expressivity of Graph Neural Networks using Localization
In this paper, we propose localized versions of Weisfeiler-Leman (WL)
algorithms in an effort to both increase the expressivity, as well as decrease
the computational overhead. We focus on the specific problem of subgraph
counting and give localized versions of WL for any . We analyze the
power of Local WL and prove that it is more expressive than WL and at
most as expressive as WL. We give a characterization of patterns whose
count as a subgraph and induced subgraph are invariant if two graphs are Local
WL equivalent. We also introduce two variants of WL: Layer WL and
recursive WL. These methods are more time and space efficient than applying
WL on the whole graph. We also propose a fragmentation technique that
guarantees the exact count of all induced subgraphs of size at most 4 using
just WL. The same idea can be extended further for larger patterns using
. We also compare the expressive power of Local WL with other GNN
hierarchies and show that given a bound on the time-complexity, our methods are
more expressive than the ones mentioned in Papp and Wattenhofer[2022a]