On compressing weighted time-evolving graphs

Liu, Wei; Kan, Andrey; Chan, Jeffrey; Bailey, James E.; Pei, Jian; Ramamohanarao, Kotagiri

doi:10.1145/2396761.2398630

Cited by 26 publications

(12 citation statements)

References 7 publications

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“…Work on compressing dynamic graphs for storage includes lossy compression of timeevolving graphs [Henecka and Roughan 2015], and encoding of dynamic, weighted graphs as three-dimensional arrays (tensor) by reducing heterogeneity and guaranteeing compression error within bounds [Liu et al 2012]. The latter is based on hierarchical clusters of edge weights and graph compression using run-length encoding, traversing first the tensor's time dimension and second the tensor's vertex dimensions.…”

Section: Influence-based Methodsmentioning

confidence: 99%

Graph Summarization Methods and Applications

et al. 2018

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While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind, and the challenges of, graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.

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Section: Influence-based Methodsmentioning

confidence: 99%

Graph Summarization Methods and Applications

et al. 2018

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“…Threshold-moving tries to move the output threshold toward inexpensive classes such that examples with higher costs become harder to be misclassified [17]. Other similar work on this issue includes [18,19,20,24]. Although some work has been done to solve the data imbalance problem in neural network, quite few literatures related to the imbalance problem of deep network can be seen so far.…”

Section: Cimbalance Problem In Neural Networkmentioning

confidence: 99%

Training deep neural networks on imbalanced data sets

Wang

Liu

et al. 2016

2016 International Joint Conference on Neural Networks (IJCNN)

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376

230

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Deep learning has become increasingly popular in both academic and industrial areas in the past years. Various domains including pattern recognition, computer vision, and natural language processing have witnessed the great power of deep networks. However, current studies on deep learning mainly focus on data sets with balanced class labels, while its performance on imbalanced data is not well examined. Imbalanced data sets exist widely in real world and they have been providing great challenges for classification tasks. In this paper, we focus on the problem of classification using deep network on imbalanced data sets. Specifically, a novel loss function called mean false error together with its improved version mean squared false error are proposed for the training of deep networks on imbalanced data sets. The proposed method can effectively capture classification errors from both majority class and minority class equally. Experiments and comparisons demonstrate the superiority of the proposed approach compared with conventional methods in classifying imbalanced data sets on deep neural networks.

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“…The goal is to either reduce storage and manipulation costs, or simplify structure. Summarizing temporal networks has not seen much work, except recent papers based on bits-storage-compression [13], or extracting a list of recurrent sub-structures over time [22]. Unlike these, we are the first to focus on hierarchical condensation: using structural merges, giving a smaller propagation-equivalent temporal network.…”

Section: Related Workmentioning

confidence: 99%

Condensing Temporal Networks using Propagation

Adhikari¹,

Zhang²,

Bharadwaj³

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Modern networks are very large in size and also evolve with time. As their size grows, the complexity of performing network analysis grows as well. Getting a smaller representation of a temporal network with similar properties will help in various data mining tasks. In this paper, we study the novel problem of getting a smaller diffusion-equivalent representation of a set of time-evolving networks.We first formulate a well-founded and general temporalnetwork condensation problem based on the so-called system-matrix of the network. We then propose NetCondense, a scalable and effective algorithm which solves this problem using careful transformations in sub-quadratic running time, and linear space complexities. Our extensive experiments show that we can reduce the size of large real temporal networks (from multiple domains such as social, co-authorship and email) significantly without much loss of information. We also show the wide-applicability of NetCondense by leveraging it for several tasks: for example, we use it to understand, explore and visualize the original datasets and to also speed-up algorithms for the influencemaximization problem on temporal networks.

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On compressing weighted time-evolving graphs

Cited by 26 publications

References 7 publications

Graph Summarization Methods and Applications

Graph Summarization Methods and Applications

Training deep neural networks on imbalanced data sets

Condensing Temporal Networks using Propagation

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