Data structured in form of overlapping or non-overlapping sets is found in a variety of domains, sometimes explicitly but often subtly. For example, teams, which are of prime importance in social science studies are "sets of individuals"; "item sets" in pattern mining are sets; and for various types of analysis in language studies a sentence can be considered as a "set or bag of words". Although building models and inference algorithms for structured data has been an important task in the fields of machine learning and statistics, research on "set-like" data still remains less explored. Relationships between pairs of elements can be modeled as edges in a graph. However, modeling relationships that involve all members of a set, a hyperedge is a more natural representation for the set. In this work, we focus on the problem of embedding hyperedges in a hypergraph (a network of overlapping sets) to a low dimensional vector space. We propose a probabilistic deep-learning based method as well as a tensor-based algebraic model, both of which capture the hypergraph structure in a principled manner without loosing set-level information. Our central focus is to highlight the connection between hypergraphs (topology), tensors (algebra) and probabilistic models. We present a number of interesting baselines, some of which adapt existing node-level embedding models to the hyperedge-level, as well as sequence based language techniques which are adapted for set structured hypergraph topology. The performance is evaluated with a network of social groups and a network of word phrases. Our experiments show that accuracy wise our methods perform similar to those of baselines which are not designed for hypergraphs. Moreover, our tensor based method is quiet efficient as compared to deep-learning based auto-encoder method. We therefore, argue that we have proposed more general methods which are suited for hypergraphs (and therefore also for graphs) while maintaining accuracy and efficiency.
The paper studies different methods for defining hypergraph embeddings, i.e. defining vectorial representations of the set of hyperedges of a given hypergraph. It should be noted that the framework does not allow to compute a vectorial representation of a set of nodes not already given as an hyperedge. A set of methods is presented : the first one is based on an auto-encoder technique ; the second one is based on tensor decomposition ; the third one derives from sentence embedding methods. The fourth one extends over node embedding techniques and the last one use spectral methods. The two first methods use plainly the set structure of hyperedges. Experimental results are provided on semi-supervised regression tasks. They show very similar performance for all methods and variants. Also run-times are compared and the results are expected. In conclusion, the paper gives an overview of methods for computing hypernode embeddings. This is interesting in its own. Nevertheless, as the target problem on hypergraphs is left unspecified, it is difficult to infer conclusions from the study. Therefore, I am not convinced that the paper should be published in ICLR'18.
* typos
* Recent surveys on graph embeddings have been published in 2017 and should be cited as "A comprehensive survey of graph embedding ..." by Cai et al
* Preliminaries. The occurrence number R(g_i) are not modeled in the hypergraphs. A graph N_a is defined but not used in the paper.
* Section 3.1. the procedure for sampling hyperedges in the lattice shoud be given. At least, you should explain how it is made efficient when the number of nodes is large.
* Section 3.2. The method seems to be restricted to cases where the cardinality of hyperedges can take a small number of values. This is discussed in Section 3.6 but the discussion is not convincing enough.
* Section 3.3 The term Sen2vec is not common knowledge
* Section 3.3 The length of the sentences depends on the number of permutations of $k$ elements. How can you deal with large k ?
* Section 3.4 and Section 3.5. The methods proposed in these two sections should be related with previous works on hypergraph kernels. I.e. there should be mentions on the clique expansion and star expansion of hypergraphs. This leads to the question why graph embeddings methods on these expansions have not be considered in the paper.
* Section 4.1. Only hyperedeges of cardinality in [2,6] are considered. This seems a rather strong limitation and this hypothesis does not seem pertinent in many applications. 
* Section 4. For online multi-player games, hypernode embeddings only allow to evaluate existing teams, i.e. already existing as hyperedges in the input hypergraph. One of the most important problem for multi-player games is team making where team evaluation should be made for all possible teams.
* Section 5. Seems redundant with the Introduction.