Deep node ranking for neuro‐symbolic structural node embedding and classification

Text modeling and sentence selection are the fundamental steps of a typical extractive document summarization algorithm. The common text modeling method connects a pair of sentences based on their similarities. Even thought it can effectively represent the sentence similarity graph of given document(s) its big drawback is a large time complexity of $O(n^2)$, where n represents the number of sentences. The quadratic time complexity makes it impractical for large documents. In this paper we propose the fast approximation algorithms for the text modeling and the sentence selection. Our text modeling algorithm reduces the time complexity to near-linear time by rapidly finding the most similar sentences to form the sentences similarity graph. In doing so we utilized Locality-Sensitive Hashing, a fast algorithm for the approximate nearest neighbor search. For the sentence selection step we propose a simple memory-access-efficient node ranking method based on the idea of scanning sequentially only the neighborhood arrays. Experimentally, we show that sacrificing a rather small percentage of recall and precision in the quality of the produced summary can reduce the quadratic to sub-linear time complexity. We see the big potential of proposed method in text summarization for mobile devices and big text data summarization for internet of things on cloud. In our experiments, beside evaluating the presented method on the standard general and query multi-document summarization tasks, we also tested it on few alternative summarization tasks including general and query, timeline, and comparative summarization.

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Graph analysis of network flow connectivity behaviors

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES ◽

10.3906/elk-1808-148 ◽

2019 ◽

pp. 861-877

Author(s):

HANGYU HU ◽

XUEMENG ZHAI ◽

MINGDA WANG ◽

GUANGMIN HU

Keyword(s):

Behavior Analysis ◽

Network Flow ◽

Graph Mining ◽

Network Flows ◽

Flow Behavior ◽

Edge Weight ◽

Event Identification ◽

Node Ranking ◽

Graph Simplification ◽

Network Event

Graph-based approaches have been widely employed to facilitate in analyzing network flow connectivity behaviors, which aim to understand the impacts and patterns of network events. However, existing approaches suffer from lack of connectivity-behavior information and loss of network event identification. In this paper, we propose network flow connectivity graphs (NFCGs) to capture network flow behavior for modeling social behaviors from network entities. Given a set of flows, edges of a NFCG are generated by connecting pairwise hosts who communicate with each other. To preserve more information about network flows, we also embed node-ranking values and edge-weight vectors into the original NFCG. After that, a network flow connectivity behavior analysis framework is present based on NFCGs. The proposed framework consists of three modules: a graph simplification module based on diversified filtering rules, a graph feature analysis module based on quantitative or semiquantitative analysis, and a graph structure analysis module based on several graph mining methods. Furthermore, we evaluate our NFCG-based framework by using real network traffic data. The results show that NFCGs and the proposed framework can not only achieve good performance on network behavior analysis but also exhibit excellent scalability for further algorithmic implementations.

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