Cascading Graph Research Articles

Prediction of information cascades via content and structure proximity preserved graph level embedding

Understanding the mechanisms how one message acquires more popularity than another and modeling how it gains popularity dynamically are of tremendous interest to related AI-based decision support systems. The information cascade prediction begins benefiting from the development of deep learning on graphs. However, recent studies are generally learning the representation of nodes in the graph, which may be not suitable as cascades contain all nodes in the dissemination path as a whole. Thus, we investigate whether the whole graph of cascade could be directly embedded in low dimension and how it would be effective for predicting the future popularity. Rather than learning the representation of all nodes in the cascade, we design a framework to learn the low dimension representation of each cascade graph by constructing the content and structure proximity-based high-order graph where each node refers to each cascade. By random walk and a semi-supervised language model, the embedding of the whole cascade graph can be obtained. Our results show that the proposed method can reduce the prediction error by at least 10.29%, 22.89% and 20.01% (measured by RMSPE) respectively on three real datasets over baselines. Moreover, the running time of the model training is much less than baselines.

Interaction Strength Analysis to Model Retweet Cascade Graphs

Tracking information diffusion is a non-trivial task and it has been widely studied across different domains and platforms. The advent of social media has led to even more challenges, given the higher speed of information propagation and the growing impact of social bots and anomalous accounts. Nevertheless, it is crucial to derive a trustworthy information diffusion graph that is capable of highlighting the importance of specific nodes in spreading the original message. The paper introduces the interaction strength, a novel metric to model retweet cascade graphs by exploring users’ interactions. Initial findings showed the soundness of the approaches based on this new metric with respect to the state-of-the-art model, and its ability to generate a denser graph, revealing crucial nodes that participated in the retweet propagation. Reliable retweet graph generation will enable a better understanding of the diffusion path of a specific tweet.

Joint Modeling of Text and Networks for Cascade Prediction

A critical research problem about information cascades, which is a central topic of social network analysis, is to predict the potential influence or the future growth of cascades. Recent developments of deep learning have provided promising alternatives, which no longer rely on heavy feature engineering efforts and instead learn the representation of cascade graphs in an end-to-end manner. In reality, however, the influence of a cascade not only depends on the cascade graph and the global network structure, but also largely relies on the content of the cascade and the preferences of users. In this work, we extend the deep learning approaches to cascade prediction by jointly modeling the content and the structure of cascades. We find that text information provides a valuable addition for the learning of cascade graphs, especially when some users (nodes) have rarely participated in the past cascades. To this end, a gating mechanism is introduced to dynamically fuse the structural and textual representations of nodes based on their respective properties. Attentions are employed to incorporate the text information associated with both cascade items and nodes. Empirical experiments demonstrate that incorporating text information brings a significant improvement to cascade prediction, and that the proposed model outperforms alternative ways to combine text and networks.

Interactive segmentation of image volumes with Live Surface

Live Surface allows users to segment and render complex surfaces from 3D image volumes at interactive (sub-second) rates using a novel, cascading graph cut (CGC). Live Surface consists of two phases: (1) preprocessing for generation of a complete 3D hierarchy of tobogganed regions followed by tracking of all region surfaces; (2) user interaction in which, with each mouse movement, the volume is segmented and the 3D object is rendered at interactive rates. Interactive segmentation is accomplished by cascading through the 3D hierarchy from the top, applying graph cut successively, at each level, only to regions bordering the segmented surface from the previous level. CGC allows the entire image volume to be segmented an order of magnitude faster than existing techniques that make use of graph cut. OpenGL rendering provides for display and update of the segmented surface at interactive rates. The user selects objects by tagging voxels with either foreground (object) or background seeds. Seeds can be placed on image cross-sections or directly on the 3D rendered surface. Interaction with the rendered surface improves the user's ability to steer the segmentation, augmenting or subtracting from the current selection. Segmentation and rendering, combined, is accomplished in about 0.35s, allowing 3D surfaces to be displayed and updated dynamically as each additional seed is deposited. The immediate feedback of Live Surface allows the segmentation of 3D image volumes using an interaction paradigm similar to the Live Wire (Intelligent Scissors) tool used in 2D images.

Linking the concept of scale to studies of biological diversity: evolving approaches and tools

ABSTRACTAlthough the concepts of scale and biological diversity independently have received rapidly increasing attention in the scientific literature since the 1980s, the rate at which the two concepts have been investigated jointly has grown much more slowly. We find that scale considerations have been incorporated explicitly into six broad areas of investigation related to biological diversity: (1) heterogeneity within and among ecosystems, (2) disturbance ecology, (3) conservation and restoration, (4) invasion biology, (5) importance of temporal scale for understanding processes, and (6) species responses to environmental heterogeneity. In addition to placing the papers of this Special Feature within the context of brief summaries of the expanding literature on these six topics, we provide an overview of tools useful for integrating scale considerations into studies of biological diversity. Such tools include hierarchical and structural‐equation modelling, kriging, variable‐width buffers,k‐fold cross‐validation, and cascading graph diagrams, among others. Finally, we address some of the major challenges and research frontiers that remain, and conclude with a look to the future.

Cascade Graphs: Design, Analysis and Algorithms for Relational Joins

The focus of this work is on join optimization in relational database systems. The importance of join optimization is critically underscored by the high cost of relational joins and their frequent needs in traditional as well as emerging database applications. We demonstrate that the sequence in which the pages of the relations are accessed to process a join is a critical determinant of the join execution cost and an optimization of this sequence can lead to a significant improvement in performance over the traditional approaches. We initially develop three network structures to represent a join on two relations: page connectivity graph, cascade and block tree cascade. A page connectivity graph is a bipartite representation of the set of connected pages in the two relations according to the join predicate. To reveal the structural properties of the join, the nodes of the bipartite graph are ordered into a set of levels, and the resulting isomorphic structure is termed a cascade. From the cascade, a tree structure termed a block tree cascade is derived by selectively grouping nodes at each level of the cascade into blocks. We formulate the join as a tree traversal process, and accordingly develop efficient tree traversal algorithms. We develop a compact data structure to store the resulting access path, and provide a comprehensive analysis of the algorithms with detailed assessments of their performance. The performance evaluation demonstrates that the proposed approach can result in significant cost savings over the current join processing methods, for low to modest values of the join selectivity factor.