Interpretable Network Representations

Networks (or interchangeably graphs) have been ubiquitous across the globe and within science and engineering: social networks, collaboration networks, protein-protein interaction networks, infrastructure networks, among many others. Machine learning on graphs, especially network representation learning, has shown remarkable performance in network-based applications, such as node/graph classification, graph clustering, and link prediction. Like performance, it is equally crucial for individuals to understand the behavior of machine learning models and be able to explain how these models arrive at a certain decision. Such needs have motivated many studies on interpretability in machine learning. For example, for social network analysis, we may need to know the reasons why certain users (or groups) are classified or clustered together by the machine learning models, or why a friend recommendation system considers some users similar so that they are recommended to connect with each other. Therefore, an interpretable network representation is necessary and it should carry the graph information to a level understandable by humans. Here, we first introduce our method on interpretable network representations: the network shape. It provides a framework to represent a network with a 3-dimensional shape, and one can customize network shapes for their need, by choosing various graph sampling methods, 3D network embedding methods and shape-fitting methods. In this thesis, we introduce the two types of network shape: a Kronecker hull which represents a network as a 3D convex polyhedron using stochastic Kronecker graphs as the network embedding method, and a Spectral Path which represents a network as a 3D path connecting the spectral moments of the network and its subgraphs. We demonstrate that network shapes can capture various properties of not only the network, but also its subgraphs. For instance, they can provide the distribution of subgraphs within a network, e.g., what proportion of subgraphs are structurally similar to the whole network? Network shapes are interpretable on different levels, so one can quickly understand the structural properties of a network and its subgraphs by its network shape. Using experiments on real-world networks, we demonstrate that network shapes can be used in various applications, including (1) network visualization, the most intuitive way for users to understand a graph; (2) network categorization (e.g., is this a social or a biological network?); (3) computing similarity between two graphs. Moreover, we utilize network shapes to extend biometrics studies to network data, by solving two problems: network identification (Given an anonymized graph, can we identify the network from which it is collected? i.e., answering questions such as ``where is this anonymized graph sampled from, Twitter or Facebook? ) and network authentication (If one claims the graph is sampled from a certain network, can we verify this claim?). The overall objective of the thesis is to provide a compact, interpretable, visualizable, comparable and efficient representation of networks

Sentiment Paradoxes in Social Networks: Why Your Friends Are More Positive Than You?

Most people consider their friends to be more positive than themselves, exhibiting a Sentiment Paradox. Psychology research attributes this paradox to human cognition bias. With the goal to understand this phenomenon, we study sentiment paradoxes in social networks. Our work shows that social connections (friends, followees, or followers) of users are indeed (not just illusively) more positive than the users themselves. This is mostly due to positive users having more friends. We identify five sentiment paradoxes at different network levels ranging from triads to large-scale communities. Empirical and theoretical evidence are provided to validate the existence of such sentiment paradoxes. By investigating the relationships between the sentiment paradox and other well-developed network paradoxes, i.e., friendship paradox and activity paradox, we find that user sentiments are positively correlated to their number of friends but rarely to their social activity. Finally, we demonstrate how sentiment paradoxes can be used to predict user sentiments.Comment: The 14th International AAAI Conference on Web and Social Media (ICWSM 2020

Ginger Stimulates Hematopoiesis via Bmp Pathway in Zebrafish

) has been widely used in traditional medicine; however, to date there is no scientific research documenting the potential of ginger to stimulate hematopoiesis. expression in the caudal hematopoietic tissue area. We further confirmed that Bmp/Smad pathway mediates this hematopoiesis promoting effect of ginger by using the Bmp-activated Bmp type I receptor kinase inhibitors dorsomorphin, LND193189 and DMH1.Our study provides a strong foundation to further evaluate the molecular mechanism of ginger and its bioactive components during hematopoiesis and to investigate their effects in adults. Our results will provide the basis for future research into the effect of ginger during mammalian hematopoiesis to develop novel erythropoiesis promoting agents

Relationship between faults and hydrocarbon migration and accumulation in Huoshiling Formation volcanic rocks in Longfengshan area, Changling Fault Depression

The Longfengshan area is an important hydrocarbon enrichment area in the Changling Fault Depression. Most of the oil and gas are distributed near the fault zone. It is of great significance to discuss the relationship between faults and hydrocarbon migration and accumulation. Combined with seismic, logging, core, microscopic thin sections, fluid inclusion observations and temperature measurement data, the relationship between section morphology, fault activity characteristics and hydrocarbon migration and accumulation is comprehensively analyzed on the basis of fault static characteristics and fault zone structure identification. The results show that a large number of fractures are developed in the fault-induced fracture zone, which can effectively improve the physical properties of volcanic rocks, and the fault can be used as the dominant migration channel for hydrocarbons. The "convergent" section shape of the grade Ⅰ fault controls the main enrichment location of hydrocarbons. The fault activity matches well with the first stage of reservoir formation, and faults can be used as a dominant channel for transporting hydrocarbons. In the later stage, the fracture of the fault zone is filled with quartz and calcite, and the fault zone mainly plays a sealing role in the volcanic oil and gas reservoirs

Progressive multi-level distillation learning for pruning network

Abstract Although the classification method based on the deep neural network has achieved excellent results in classification tasks, it is difficult to apply to real-time scenarios because of high memory footprints and prohibitive inference times. Compared to unstructured pruning, structured pruning techniques can reduce the computation cost of the model runtime more effectively, but inevitably reduces the precision of the model. Traditional methods use fine tuning to restore model damage performance. However, there is still a large gap between the pruned model and the original one. In this paper, we use progressive multi-level distillation learning to compensate for the loss caused by pruning. Pre-pruning and post-pruning networks serve as the teacher and student networks. The proposed approach utilizes the complementary properties of structured pruning and knowledge distillation, which allows the pruned network to learn the intermediate and output representations of the teacher network, thus reducing the influence of the model subject to pruning. Experiments demonstrate that our approach performs better on CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets with different pruning rates. For instance, GoogLeNet can achieve near lossless pruning on the CIFAR-10 dataset with 60% pruning. Moreover, this paper also proves that using the proposed distillation learning method during the pruning process achieves more significant performance gains than after completing the pruning