Knowledge representation learning has received a lot of attention in the past few years. The success of existing methods heavily relies on the quality of knowledge graphs. The entities with few triplets tend to be learned with less expressive power.
Fortunately, there are many knowledge graphs constructed from various sources, the representations of which could contain much information. We propose an adversarial embedding transfer network ATransN, which transfers knowledge from one or more teacher knowledge graphs to a target one through an aligned entity set without explicit data leakage. Specifically, we add soft constraints on aligned entity pairs and neighbours to the existing knowledge representation learning methods. To handle the problem of possible distribution differences between teacher and target knowledge graphs, we introduce an adversarial adaption module. The discriminator of this module evaluates the degree of consistency between the embeddings of an aligned entity pair. The consistency score is then used as the weights of soft constraints. It is not necessary to acquire the relations and triplets in teacher knowledge graphs because we only utilize the entity representations. Knowledge graph completion results show that ATransN achieves better performance against baselines without transfer on three datasets, CN3l, WK3l, and DWY100k. The ablation study demonstrates that ATransN can bring steady and consistent improvement in different settings. The extension of combining other knowledge graph embedding algorithms and the extension with three teacher graphs display the promising generalization of the adversarial transfer network.
Much effort has been devoted to understand how temporal network features and the choice of the source node affect the prevalence of a diffusion process. In this work, we addressed the further question: node pairs with what kind of local and temporal
connection features tend to appear in a diffusion trajectory or path, thus contribute to the actual information diffusion. We consider the Susceptible-Infected spreading process with a given infection probability per contact on a large number of real-world temporal networks. We illustrate how to construct the information diffusion backbone where the weight of each link tells the probability that a node pair appears in a diffusion process starting from a random node. We unravel how these backbones corresponding to different infection probabilities relate to each other and point out the importance of two extreme backbones: the backbone with infection probability one and the integrated network, between which other backbones vary. We find that the temporal node pair feature that we proposed could better predict the links in the extreme backbone with infection probability one as well as the high weight links than the features derived from the integrated network. This universal finding across all the empirical networks highlights that temporal information are crucial in determining a node pairs role in a diffusion process. A node pair with many early contacts tends to appear in a diffusion process. Our findings shed lights on the in-depth understanding and may inspire the control of information spread.
A signed network represents how a set of nodes are connected by two logically contradictory types of links: positive and negative links. In a signed products network, two products can be complementary (purchased together) or substitutable (purchased
instead of each other). Such contradictory types of links may play dramatically different roles in the spreading process of information, opinion, behavior etc. In this work, we propose a Self-Avoiding Pruning (SAP) random walk on a signed network to model e.g. a users purchase activity on a signed products network. A SAP walk starts at a random node. At each step, the walker moves to a positive neighbour that is randomly selected and its previously visited node together with its negative neighbours are removed. We explored both analytically and numerically how signed network topological features influence the key performance of a SAP walk: the evolution of the pruned network resulted from the node removals, the length of a SAP walk and the visiting probability of each node. These findings in signed network models are further verified in two real-world signed networks. Our findings may inspire the design of recommender systems regarding how recommendations and competitions may influence consumers purchases and products popularity.
The Xinglong 2.16-m reflector is the first 2-meter class astronomical telescope in China. It was jointly designed and built by the Nanjing Astronomical Instruments Factory (NAIF), Beijing Astronomical Observatory (now National Astronomical Observator
ies, Chinese Academy of Sciences, NAOC) and Institute of Automation, Chinese Academy of Sciences in 1989. It is Ritchey-Chr{e}tien (R-C) reflector on an English equatorial mount and the effective aperture is 2.16 meters. It had been the largest optical telescope in China for $sim18$ years until the Guoshoujing Telescope (also called Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST) and the Lijiang 2.4-m telescope were built. At present, there are three main instruments on the Cassegrain focus available: the Beijing Faint Object Spectrograph and Camera (BFOSC) for direct imaging and low resolution ($Rsim500-2000$) spectroscopy, the spectrograph made by Optomechanics Research Inc. (OMR) for low resolution spectroscopy (the spectral resolutions are similar to those of BFOSC) and the fiber-fed High Resolution Spectrograph (HRS, $Rsim30000-65000$). The telescope is widely open to astronomers all over China as well as international astronomical observers. Each year there are more than 40 ongoing observing projects, including 6-8 key projects. Recently, some new techniques and instruments (e.g., astro-frequency comb calibration system, polarimeter and adaptive optics) have been or will be tested on the telescope to extend its observing abilities.
