An approach to automatic display layout using combinatorial optimization algorithms

The introduction of automatic display layout (ADL), i.e. the automatic placing and sizing of windows in a window-oriented graphical user interface, is a major contribution towards an improved user interface. Our approach to ADL is to treat this problem as a combinatorial optimization problem. In this article we describe the concepts we used for implementing an experimental system which controls the computer screen contents and its layout. We give two examples of different standard applications into which we included ADL successfully, namely hypertext for a window layout problem and graph-browser for a hierarchical graph layout problem within a particular window. The results show that automatic (and tool independent) display layout will be possible in the near future even in an interactive environment.     

A Visualization Framework and User Studies for Overloaded Orthogonal Drawings

Overloaded orthogonal drawing (OOD) is a recent graph visualization style specifically conceived for directed graphs. It merges the advantages of some popular drawing conventions like layered drawings and orthogonal drawings, and provides additional support for some common analysis tasks. We present a visualization framework called DAGView, which implements algorithms and graphical features for the OOD style. Besides the algorithm for acyclic digraphs, the DAGView framework implements extensions to visualize both digraphs with cycles and undirected graphs, with the additional possibility of taking into account user preferences and constraints. It also supports an interactive visualization of clustered digraphs, based on the use of strongly connected components. Moreover, we describe an experimental user study, aimed to investigate the usability of OOD within the DAGView framework. The results of our study suggest that OOD can be effectively exploited to perform some basic tasks of analysis in a faster and more accurate way when compared to other drawing styles for directed graphs.     

Joint auto-weighted graph fusion and scalable semi-supervised learning

Graph carries out a key role in graph-based semi-supervised label propagation, as it clarifies the structure of the data manifold. The performance of label propagation methods depends on the adopted graph and can be enhanced by merging different graphs that are obtained from multiple sources of information. While there exist algorithms that perform graph fusion they have several weaknesses. Most of these algorithms define graph fusion and label propagation as two separate tasks. Moreover, when the number of data expands, these strategies are not well-suited due to the use of transductive learning in the label propagation phase which makes the label prediction for unseen samples difficult. Furthermore, very few algorithms extract the information contained in the label space. Additionally, most of the graph fusion techniques adopt equal or static weights for different views, which is not the best choice as distinctive features (hence different graphs) contain various information. To overcome these shortcomings, we propose an Auto-weighted Multi-view Semi-Supervised Learning method (AMSSL), which is based on an inductive learning algorithm (i.e., Flexible Manifold Embedding) and profited a projection matrix for predicting the labels of out-of-sample data. The proposed AMSSL method represents a unified framework that dynamically fuses various information obtained from different features and also from the label space and adaptively designates appropriate weights according to the usefulness of each view. Our experimental results on seven small and large image datasets demonstrate the superiority of the proposed method compared to the use of one single feature and other state-of-the-art graph fusion methods.     

基于图深度学习的金融文本多标签分类算法

金融文本多标签分类算法可以根据用户需求在海量金融资讯中实现信息检索。为进一步提升金融文本标签识别能力,建模金融文本多标签分类中标签之间的相关性,提出基于图深度学习的金融文本多标签分类算法。图深度学习通过深度网络学习局部和全局的图结构特征,可以刻画节点之间的复杂关系。通过建模标签关联实现标签之间的知识迁移,是构造具有强泛化能力算法的关键。所提算法结合标签之间的关联信息,采用基于双向门控循环网络和标签注意力机制得到的新闻文本对应不同标签的特征表示,通过图神经网络学习标签之间的复杂依赖关系。在真实数据集上的实验结果表明,显式建模标签之间的相关性能够极大地增强模型的泛化能力,在尾部标签上的性能提升尤其显著,相比CAML、BIGRU-LWAN和ZACNN算法,该算法在所有标签和尾部标签的宏观F1值上最高提升3.1%和6.9%。     

Discovering Typed Communities in Mobile Social Networks

Mobile social networks,which consist of mobile users who communicate with each other using cell phones,are reflections of people’s interactions in social lives.Discovering typed communities(e.g.,family communities or corporate communities) in mobile social networks is a very promising problem.For example,it can help mobile operators to determine the target users for precision marketing.In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users.We use the user logs stored by mobile operators,including communication and user movement records,to collectively label all the relationships in a network,by employing an undirected probabilistic graphical model,i.e.,conditional random fields.Then we use two methods to discover typed communities based on the results of relationship labeling:one is simply retaining or cutting relationships according to their labels,and the other is using sophisticated weighted community detection algorithms.The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.     

基于图元的测试信息描述技术研究

本文分析可视化自动测试系统中测试信息描述的方法,简单介绍基于图元的可视化测试平台的组成结构。然后,针对测试设计过程中硬件测试环境设计、测试软件的流程设计、输入输出数据设计这三个步骤,使用XML模式分别定义了相应的描述语言。为可视化界面信息的描述设计了图描述语言,为测试信息的描述设计了测试描述语言。测试设计与生成的测试程序之间通过XML脚本文件松耦合,不仅便于与其它系统交换数据,而且便于系统本身扩展和更新。     

Dynamic map labeling

We address the problem of filtering, selecting and placing labels on a dynamic map, which is characterized by continuous zooming and panning capabilities. This consists of two interrelated issues. The first is to avoid label popping and other artifacts that cause confusion and interrupt navigation, and the second is to label at interactive speed. In most formulations the static map labeling problem is NP-hard, and a fast approximation might have O(n log n) complexity. Even this is too slow during interaction, when the number of labels shown can be several orders of magnitude less than the number in the map. In this paper we introduce a set of desiderata for "consistent" dynamic map labeling, which has qualities desirable for navigation. We develop a new framework for dynamic labeling that achieves the desiderata and allows for fast interactive display by moving all of the selection and placement decisions into the preprocessing phase. This framework is general enough to accommodate a variety of selection and placement algorithms. It does not appear possible to achieve our desiderata using previous frameworks. Prior to this paper, there were no formal models of dynamic maps or of dynamic labels; our paper introduces both. We formulate a general optimization problem for dynamic map labeling and give a solution to a simple version of the problem. The simple version is based on label priorities and a versatile and intuitive class of dynamic label placements we call "invariant point placements". Despite these restrictions, our approach gives a useful and practical solution. Our implementation is incorporated into the G-Vis system which is a full-detail dynamic map of the continental USA. This demo is available through any browser     

Statechart Modeling with Fujaba

This paper is a small case study on a simple visual language. We use the Fujaba approach to exemplify, how the different requirements of an environment for such a visual language may be addressed using Fujaba graph transformations. This covers abstract and concrete syntax, static and operational semantics, and model transformations. This case study shows, how the more sophisticated language elements of Fujaba may be exploited in modeling complex aspects of the statechart environment. In addition, we address some not graph grammar related aspects in building such an environment, e.g. the graphical user interface and multi-user support.     

Automatic multi-partite graph generation from arbitrary data

In this paper we present a generic model for automatic generation of basic multi-partite graphs obtained from collections of arbitrary input data following user indications. The paper also presents GraphGen, a tool that implements this model. The input data is a collection of complex objects composed by a set or list of heterogeneous elements. Our tool provides a simple interface for the user to specify the types of nodes that are relevant for the application domain in each case. The nodes and the relationships between them are derived from the input data through the application of a set of derivation rules specified by the user. The resulting graph can be exported in the standard GraphML format so that it can be further processed with other graph management and mining systems. We end by giving some examples in real scenarios that show the usefulness of this model.     

A discriminative graph inferring framework towards weakly supervised image parsing

In this paper, we focus on the task of assigning labels to the over-segmented image patches in a weakly supervised manner, in which the training images contain the labels but do not have the labels’ locations in the images. We propose a unified discriminative graph inferring framework by simultaneously inferring patch labels and learning the patch appearance models. On one hand, graph inferring reasons the patch labels by a graph propagation procedure. The graph is constructed by connecting the nearest neighbors which share the same image label, and multiple correlations among patches and image labels are imposed as constraints to the inferring. On the other hand, for each label, the patches which do not contain the target label are adopted as negative samples to learn the appearance model. In this way, the predicted labels will be more accurate in the propagation. Graph inferring and the learned patch appearance models are finally embedded to complement each other in one unified formulation. Experiments on three public datasets demonstrate the effectiveness of our method in comparison with other baselines.     

地图文本注记问题的遗传算法求解

针对地理信息系统中对图形进行文本注记难,大多数已知工具都产生一定数量的注记重叠并且需要人工调节的问题,采用遗传算法解决注记冲突.综合注记信息与线状图形信息的特征,提出了以重叠注记构造位串编码,采用沿折线随机游走的方式构造变异算子,并考虑两个目标,一个是在注记中避免重叠,另一个是提高注记放置的美术性,采用梯形模糊数实现美学度量.初步测试表明,所提算法在快速文本注记和提高美学度上非常有效.     

GO: A cluster algorithm for graph visualization

As we are in the big data age, graph data such as user networks in Facebook and Flickr becomes large. How to reduce the visual complexity of a graph layout is a challenging problem. Clustering graphs is regarded as one of effective ways to address this problem. Most of current graph visualization systems, however, directly use existing clustering algorithms that are not originally developed for the visualization purpose. For graph visualization, a clustering algorithm should meet specific requirements such as the sufficient size of clusters, and automatic determination of the number of clusters. After identifying the requirements of clustering graphs for visualization, in this paper we present a new clustering algorithm that is particularly designed for visualization so as to reduce the visual complexity of a layout, together with a strategy for improving the scalability of our algorithm. Experiments have demonstrated that our proposed algorithm is capable of detecting clusters in a way that is required in graph visualization.     

Ensemble methods for multi-label classification

Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, instead of randomly choosing subsets, we select the minimum required subsets of k labels that cover all labels and meet additional constraints such as coverage of inter-label correlations. Construction of the cover is achieved by formulating the subset selection as a minimum set covering problem (SCP) and solving it by using approximation algorithms. Every cover needs only to be prepared once by offline algorithms. Once prepared, a cover may be applied to the classification of any given multi-label dataset whose properties conform with those of the cover. The contribution of this paper is two-fold. First, we introduce SCP as a general framework for constructing label covers while allowing the user to incorporate cover construction constraints. We demonstrate the effectiveness of this framework by proposing two construction constraints whose enforcement produces covers that improve the prediction performance of random selection by achieving better coverage of labels and inter-label correlations. Second, we provide theoretical bounds that quantify the probabilities of random selection to produce covers that meet the proposed construction criteria. The experimental results indicate that the proposed methods improve multi-label classification accuracy and stability compared to the RAKEL algorithm and to other state-of-the-art algorithms.     

AutoGen: Easing model management through two levels of abstraction

Due to its extensive potential applications, model management has attracted many research interests and gained great progress. To provide easy-to-use interfaces, we have proposed a graph transformation-based model management approach that provides intuitive interfaces for manipulation of graphical data models. The approach consists of two levels of graphical operators: low-level customizable operators and high-level generic operators, both of which consist of a set of graph transformation rules. Users need to program or tune the low-level operators for desirable results. To further improve the ease-of-use of the graphical model management, automatic generation of low level of operators is highly desirable. The paper formalizes specifications of low- and high-level operators and proposes a generator to automatically transform high-level operators into low-level operators upon specific input data models. Based on graph transformation theoretical foundation, we design an algorithm for the generator to automatically produce low-level operators from input data models and mappings according to a high-level operator. The generator, called AutoGen, therefore eliminates many tedious specifications and thus eases the use of the graphical model management system.     

New heuristic approach to the calculation of general recoupling coefficients

General angular momentum recoupling coefficients can be expressed as a summation formula over products of 6-j coefficients. Yutsis, Levinson and Vanagas developed graphical techniques for representing the general recoupling coefficient as a cubic graph and they describe a set of reduction rules on such a graph which allow a stepwise generation of the corresponding summation formula. In this paper we present a new heuristic approach to earlier reduction algorithms based on these techniques. In particular the heuristic tries in each step to make an ‘intelligent’ choice of which specific reduction rule to apply on which part of the graph. This approach leads to good results: in many cases we get a more efficient summation formula than the other algorithms. The program is written in Java and offers a platform-independent graphical user front end which allows user-friendly manipulation of recoupling coefficients as well as a detailed follow-up of the reduction process.     

Matching graphs with unique node labels

A special class of graphs is introduced in this paper. The graphs belonging to this class are characterised by the existence of unique node labels. A number of matching algorithms for graphs with unique node labels are developed. It is shown that problems such as graph isomorphism, subgraph isomorphism, maximum common subgraph (MCS) and graph edit distance (GED) have a computational complexity that is only quadratic in the number of nodes. Moreover, computing the median of a set of graphs is only linear in the cardinality of the set. In a series of experiments, it is demonstrated that the proposed algorithms run very fast in practice. The considered class makes the matching of large graphs, consisting of thousands of nodes, computationally tractable. We also discuss an application of the considered class of graphs and related matching algorithms to the classification and detection of abnormal events in computer networks.     

A graphical criterion of planarity for RNA secondary structures with pseudoknots in Rivas–Eddy class

An RNA secondary structure is considered to be planar if its arc graph can be embedded into a plane without edge crossing. In this paper, a graphical criterion of planarity is presented based on graphical composition for RNA secondary structures with pseudoknots in Rivas–Eddy Class. Effective planar testing algorithms are introduced based on our graphical criterion.     

Trust-aware recommendation based on heterogeneous multi-relational graphs fusion

Users’ trust relations have a significant influence on their choice towards different products. However, few recommendation or prediction algorithms both consider users’ social trust relations and item-related knowledge, which makes them difficult to cope with cold start and the data sparsity problems. In this paper, we propose a novel trust-ware recommendation method based on heterogeneous multi-relational graphs fusion, termed as T-MRGF. In contrast with other traditional methods, it fuses the user-related and item-related graphs with the user–item interaction graph and fully utilizes the high-level connections existing in heterogeneous graphs. Specifically, we first establish the user–user trust relation graph, user–item interaction graph and item–item knowledge graph, and the user feature and item feature, which have been obtained from the user–item graph, are used as the input of the user-related graph and the item-related graph respectively. The fusion is achieved through the cascade of feature vectors before and after feature propagation. In this way, the heterogeneous multi-relational graphs are fused for the feature propagation, which largely refines the user and item representation for model prediction. Simulation results show that the proposed method significantly improve the recommendation performance compared to the state-of-the-art KG-based algorithms both in accuracy and training efficiency.     

3Desque: interface elements for a 3D graphical user interface

This paper explores some design possibilities and constraints for 3D enhancements of graphical user interfaces (GUIs). This is done with the aim of conveying more information in less screen space, while avoiding visual clutter. Design elements include the use of ‘slapped‐back’ windows obtained by projecting windows and icons into three dimensions; ‘trays’, an alternative to folders or piles; the use of transparent ‘beams’ for indicating hierarchy; and the use of ‘periphs’ for a form of fisheye projection on window borders. In addition we employ shading and shadows to enhance the interface, and automatic placement algorithms to prevent visually confusing occlusion. By considering these approaches together, rather than in isolation, the interactions between the different modifications are made explicit. Copyright © 1999 John Wiley & Sons, Ltd.