Structural Deep Embedding for Hyper-Networks

Network embedding has recently attracted lots of attentions in data mining. Existing network embedding methods mainly focus on networks with pairwise relationships. In real world, however, the relationships among data points could go beyond pairwise, i.e., three or more objects are involved in each relationship represented by a hyperedge, thus forming hyper-networks. These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge. These indecomposable hyperedges are especially common in heterogeneous networks. In this paper, we propose a novel Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with indecomposable hyperedges. More specifically, we theoretically prove that any linear similarity metric in embedding space commonly used in existing methods cannot maintain the indecomposibility property in hyper-networks, and thus propose a new deep model to realize a non-linear tuplewise similarity function while preserving both local and global proximities in the formed embedding space. We conduct extensive experiments on four different types of hyper-networks, including a GPS network, an online social network, a drug network and a semantic network. The empirical results demonstrate that our method can significantly and consistently outperform the state-of-the-art algorithms.Comment: Accepted by AAAI 1

Investigations of supernovae and supernova remnants in the era of SKA

Two main physical mechanisms are used to explain supernova explosions: thermonuclear explosion of a white dwarf(Type Ia) and core collapse of a massive star (Type II and Type Ib/Ic). Type Ia supernovae serve as distance indicators that led to the discovery of the accelerating expansion of the Universe. The exact nature of their progenitor systems however remain unclear. Radio emission from the interaction between the explosion shock front and its surrounding CSM or ISM provides an important probe into the progenitor star's last evolutionary stage. No radio emission has yet been detected from Type Ia supernovae by current telescopes. The SKA will hopefully detect radio emission from Type Ia supernovae due to its much better sensitivity and resolution. There is a 'supernovae rate problem' for the core collapse supernovae because the optically dim ones are missed due to being intrinsically faint and/or due to dust obscuration. A number of dust-enshrouded optically hidden supernovae should be discovered via SKA1-MID/survey, especially for those located in the innermost regions of their host galaxies. Meanwhile, the detection of intrinsically dim SNe will also benefit from SKA1. The detection rate will provide unique information about the current star formation rate and the initial mass function. A supernova explosion triggers a shock wave which expels and heats the surrounding CSM and ISM, and forms a supernova remnant (SNR). It is expected that more SNRs will be discovered by the SKA. This may decrease the discrepancy between the expected and observed numbers of SNRs. Several SNRs have been confirmed to accelerate protons, the main component of cosmic rays, to very high energy by their shocks. This brings us hope of solving the Galactic cosmic ray origin's puzzle by combining the low frequency (SKA) and very high frequency (Cherenkov Telescope Array: CTA) bands' observations of SNRs.Comment: To be published in: "Advancing Astrophysics with the Square Kilometre Array", Proceedings of Science, PoS(AASKA14