Dynamic graphs are common in many applications to conveniently model heterogeneous data integrated from multiple sources. We study the monitoring of predictive events in dynamic graphs. Treating the problem as a continuous multi-label classification, we use deep metric learning to manage the embedding space and to create spherical codes where each codeword is an embedding vector representing a cluster of data state embeddings with the same results of the predictive events. By continuously training data embeddings from a dynamic graph neural network (DGNN) model and a code generator together, our method, called SCode, achieves significantly better accuracy than DGNN baselines. Moreover, SCode is also about twice as fast as the DGNN baselines, owing to its efficient matching between data state embedding and codewords for multiple events together. Finally, our training sample complexity analysis also sheds light on the generalizability of the online learning.

We observe the need for continuous, online training of dynamic graph neural network (DGNN) models while at the same time using them to answer continuous predictive queries as data streams in. This implies significant training-time and memory costs. Along with the DGNN model learning, we simultaneously learn a weight/priority distribution over the nodes via a randomized online algorithm. In turn, the DGNN is continuously trained/learned by sampling nodes from the learned distribution and performing the chosen nodes' partitions of training work. We also devise a novel graph Kernel Density Estimation technique to smooth the distribution and improve the learning quality. Our experiments show that continuous online learning is much needed for graph streams and our approach significantly improves the standard DGNN models-to achieve the same accuracy, the training time ranges from several times to two orders of magnitude shorter, and the maximum memory consumption is several times to 20 times smaller.

We study a novel problem of continuously predicting a number of user-subscribed continuous analytics targets (CATs) in dynamic networks. Our architecture includes any dynamic graph neural network model as the back end applied over the network data, and per CAT front end models that return results with their confidence to users. We devise a data filtering algorithm that feeds a provably optimal subset of data in the embedding space from back end model to front end models. Secondly, to ensure fairness in terms of query result accuracy for different CATs and users, we propose a fairness metric and a fairness-aware training scheduling algorithm, along with accuracy guarantees on fairness estimation. Our experiments over five real-world datasets show that our proposed solution is effective, efficient, fair, extensible, and adaptive.

Heterogeneous graph streams are very common in the applications today. Although representation learning has advantages in prediction accuracy, it is inherently deficient in the abilities to interpret or to reason well. It has long been realized as far back as in 1990 by Marvin Minsky that connectionist networks and symbolic rules should co-exist in a system and overcome the deficiencies of each other. The goal of this paper is to show that it is feasible to simultaneously and efficiently perform representation learning (for connectionist networks) and rule learning spontaneously out of the same online training process for graph streams. We devise such a system called RL$^2$, and show, both analytically and empirically, that it is highly efficient and responsive for graph streams, and produces good results for both representation learning and rule learning in terms of prediction accuracy and returning top-quality rules for interpretation and building dynamic Bayesian networks.

Functional description information refers to the texts that describe the functionality or performance characteristics of a certain object. This type of information is of great potential value for the field of intelligence discovery. Thus automatically and accurately identifying this information from large amounts of texts on the web is very important. In this paper we reduce the functional description problem to a binary classification task deciding whether the input sentence is a functional description sentence or not. However, there exist lots of comment texts in the web data, which are semantically very similar to description texts, making our task quite difficult. Also, existing methods only provide general sentence representation models, which can't lead to targeted ways to solve our problem. Therefore, to address the problem, we not only exploit contexts, like many other previous work did, but also introduce indicator word information to learn rich representations. And in order to incorporate them both, we propose two models, namely ICNet(multi-tasks) and ICNet(ensemble). ICNet(multitasks) exploits them jointly in a integrated process of learning representations, while ICNet(ensemble) exploits them by two respective but concatenated sub-models. Experimental results on the collected real-world dataset indicate that both ICNet(multitasks) and ICNet(ensemble) achieve higher F1 scores compared with FaxtText, CNN, RNN, LSTM and Bi-LSTM, QuickThought models on this task.

Software as a Service (SaaS) has become an important application development and service delivery model. Among different architectures, multi-tenant architecture (MTA) not only has advantage on maintenance, but also increases resource utilization by sharing instances. However, sharing instances brings challenges to the security of the service. As one of the three principal properties of the security, availability receives more and more attentions. Recently, there are extensive efforts on technical methods to implement a secure multi-tenant SaaS, but few works on the modeling and analysis of its availability. In this paper, we firstly present the availability issues of the multi-tenant SaaS. Two important mechanisms to implement the MTA SaaS are then introduced: network isolation and database sharing. After that, a stochastic Petri net (SPN) model is developed to analyze the availability. Specific metrics are proposed to measure the availability both from the aspects of the system and the tenant. To extend the SPN model for large scale analysis, we solve the state space explosion problem of SPN model based on the theory of Markov chain aggregation. Finally, numerical results are provided to demonstrate the effectiveness of the SPN model and the analysis is efficient.