Graph View Research Articles

Augmented Intention Model for Next-Location Prediction from Graphical Trajectory Context

Human trajectory prediction is an essential task for various applications such as travel recommendation, location-sensitive advertisement, and traffic planning. Most existing approaches are sequential-model based and produce a prediction by mining behavior patterns. However, the effectiveness of pattern-based methods is not as good as expected in real-life conditions, such as data sparse or data missing. Moreover, due to the technical limitations of sensors or the traffic situation at the given time, people going to the same place may produce different trajectories. Even for people traveling along the same route, the observed transit records are not exactly the same. Therefore trajectories are always diverse, and extracting user intention from trajectories is difficult. In this paper, we propose an augmented-intention recurrent neural network (AI-RNN) model to predict locations in diverse trajectories. We first propose three strategies to generate graph structures to demonstrate travel context and then leverage graph convolutional networks to augment user travel intentions under graph view. Finally, we use gated recurrent units with augmented node vectors to predict human trajectories. We experiment with two representative real-life datasets and evaluate the performance of the proposed model by comparing its results with those of other state-of-the-art models. The results demonstrate that the AI-RNN model outperforms other methods in terms of top-k accuracy, especially in scenarios with low similarity.

Graph Queries

We review various graph query language fragments that are both theoretically tractable and practically relevant. We focus on the most expressive one that retains these properties and use it as a stepping stone to examine the underpinnings of graph query evaluation along graph view maintenance. Further broadening the scope of the discussion, we then consider alternative processing techniques for graph queries, based on graph summarization and path query learning. We conclude by pinpointing the open research directions in this emerging area.

Natural emergence of a core structure in networks via clique percolation

Networks are often presented as containing a ``core'' and a ``periphery.'' The existence of a core suggests that some vertices are central and form the skeleton of the network, to which all other vertices are connected. An alternative view of graphs is through communities. Multiple measures have been proposed for dense communities in graphs, the most classical being $k$-cliques, $k$-cores, and $k$-plexes, all presenting groups of tightly connected vertices. We here show that the edge number thresholds for such communities to emerge and for their percolation into a single dense connectivity component are very close, in all networks studied. These percolating cliques produce a natural core and periphery structure. This result is generic and is tested in configuration models and in real-world networks. This is also true for $k$-cores and $k$-plexes. Thus, the emergence of this connectedness among communities leading to a core is not dependent on some specific mechanism but a direct result of the natural percolation of dense communities.

Spectral Filter Tracking.

Visual object tracking is a challenging computer vision task with numerous real-world applications. In this paper, we propose a simple but efficient Spectral Filter Tracking (SFT) method from the view of graph, where each candidate image region is modeled as a pixelwise grid graph. Instead of the conventional graph matching, we formulate the tracking as a plain least square regression problem of learning spectral filters on graphs to predict an optimal vertex, which indicates the center of the target. To bypass computationally expensive eigenvalue decomposition on graph Laplacian L, we parameterize spectral graph filters as a polynomial of L to aggregate local graph features according to spectral graph theory, in which Lk exactly encodes a k-hop local neighborhood of each vertex. Thus, different from the holistic regression in those correlation filter based methods, SFT can operate on localized regions around a pixel (i.e., a vertex), which can effectively reduce the influence of local variations and cluttered backgrounds. Furthermore, we observe that the correlation filter tracking may be viewed as a specific case of our proposed spectral filtering method. The implementation of SFT can simply boil down to only a few line codes, but surprisingly it beats the correlation filter based model with the same feature input, and achieves the state-of-the-art performance on OTB-2015 and VOT2016 under the same feature extraction strategy.

Anomaly analysis and visualization for dynamic networks through spatiotemporal graph segmentations

With recent technology advance in Internet of Things (IoT) that involves human, sensors, and mobile devices, networks are not only growing much larger but more complex and dynamic in nature. The spatiotemporal dynamics of networks are represented by both topological changes and temporal shifts of attribute information associated with network components. Understanding the pattern and trend of dynamic networks is increasingly important. While data mining approaches are generally useful in analyzing the statistical properties of networks, there has been recent trend to consider bringing human into the loop, and to examine how feedback from visualizations of large-scale dynamic networks can further improve data mining and machine learning. Traditional visualization methods based on animation and sequences of snapshot graphs are also limited by human cognitive capability. We present a dynamic network analysis and visualization (DNAV) tool which explores the spatiotemporal dimensions of graph components. In particular, nodes and edges are augmented with spatiotemporal segmentation based on both topological and attribute dynamics (e.g., time and locations of connectivity). To further facilitate analysis of large dynamic networks, DNAV includes statistical dynamic overviews alongside graph views, as well as data filtering modules for scalable analysis. Using case studies on public datasets, we demonstrate the effectiveness of DNAV in understanding and analyzing anomalies in dynamic networks such as computer communication networks.

Certified Graph View Maintenance with Regular Datalog

AbstractWe employ the Coq proof assistant to develop a mechanically-certified framework for evaluating graph queries and incrementally maintaining materialized graph instances, also called views. The language we use for defining queries and views is Regular Datalog (RD) – a notable fragment of non-recursive Datalog that can express complex navigational queries, with transitive closure as native operator. We first design and encode the theory of RD and then mechanize a RD-specific evaluation algorithm capable of fine-grained, incremental graph view computation, which we prove sound with respect to the declarative RD semantics. By using the Coq extraction mechanism, we test an OCaml version of the verified engine on a set of preliminary benchmarks. Our development is particularly focused on leveraging existing verification and notational techniques to: a) define mechanized properties that can be easily understood by logicians and database researchers and b) attain formal verification with limited effort. Our work is the first step towards a unified, machine-verified, formal framework for dynamic graph query languages and their evaluation engines.

Utilizing Provenance in Reusable Research Objects

Science is conducted collaboratively, often requiring the sharing of knowledge about computational experiments. When experiments include only datasets, they can be shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers (DOIs). An experiment, however, seldom includes only datasets, but more often includes software, its past execution, provenance, and associated documentation. The Research Object has recently emerged as a comprehensive and systematic method for aggregation and identification of diverse elements of computational experiments. While a necessary method, mere aggregation is not sufficient for the sharing of computational experiments. Other users must be able to easily recompute on these shared research objects. Computational provenance is often the key to enable such reuse. In this paper, we show how reusable research objects can utilize provenance to correctly repeat a previous reference execution, to construct a subset of a research object for partial reuse, and to reuse existing contents of a research object for modified reuse. We describe two methods to summarize provenance that aid in understanding the contents and past executions of a research object. The first method obtains a process-view by collapsing low-level system information, and the second method obtains a summary graph by grouping related nodes and edges with the goal to obtain a graph view similar to application workflow. Through detailed experiments, we show the efficacy and efficiency of our algorithms.

The role of public goods in planetary evolution.

Biological public goods are broadly shared within an ecosystem and readily available. They appear to be widespread and may have played important roles in the history of life on Earth. Of particular importance to events in the early history of life are the roles of public goods in the merging of genomes, protein domains and even cells. We suggest that public goods facilitated the origin of the eukaryotic cell, a classic major evolutionary transition. The recognition of genomic public goods challenges advocates of a direct graph view of phylogeny, and those who deny that any useful phylogenetic signal persists in modern genomes. Ecological spillovers generate public goods that provide new ecological opportunities.This article is part of the themed issue 'Reconceptualizing the origins of life'.

Updates to the RNA mapping database (RMDB), version 2

Chemical mapping is a broadly utilized technique for probing the structure and function of RNAs. The volume of chemical mapping data continues to grow as more researchers routinely employ this information and as experimental methods increase in throughput and information content. To create a central location for these data, we established an RNA mapping database (RMDB) 5 years ago. The RMDB, which is available at http://rmdb.stanford.edu, now contains chemical mapping data for over 800 entries, involving 134 000 natural and engineered RNAs, in vitro and in cellulo. The entries include large data sets from multidimensional techniques that focus on RNA tertiary structure and co-transcriptional folding, resulting in over 15 million residues probed. The database interface has been redesigned and now offers interactive graphical browsing of structural, thermodynamic and kinetic data at single-nucleotide resolution. The front-end interface now uses the force-directed RNA applet for secondary structure visualization and other JavaScript-based views of bar graphs and annotations. A new interface also streamlines the process for depositing new chemical mapping data to the RMDB.

TurtleEditor: A Web-Based RDF Editor to Support Distributed Ontology Development on Repository Hosting Platforms

Ontologies are increasingly being developed on web-based repository hosting platforms such as GitHub. Accordingly, there is a demand for ontology editors which can be easily connected to the hosted repositories. TurtleEditor is a web-based RDF editor that provides this capability and supports the distributed development of ontologies on repository hosting platforms. It offers features such as syntax checking, syntax highlighting, and auto completion, along with a SPARQL endpoint to query the ontology. Furthermore, TurtleEditor integrates a visual editing view that allows for the graphical manipulation of the RDF graph and includes some basic clustering functionality. The text and graph views are constantly synchronized so that all changes to the ontology are immediately propagated and the views are updated accordingly. The results of a user study and performance tests show that TurtleEditor can indeed be effectively used to support the distributed development of ontologies on repository hosting platforms.

APT malware static trace analysis through bigrams and graph edit distance

Research and business organizations are vulnerable to attack by malware, particularly advanced persistent threat malware tailored for a specific target. Malware identification is made more difficult because samples can be subtly altered to avoid detection by methods that check for an identical match to known code. Different versions of an original piece of malware form a malware family. When new malicious software is identified, reverse engineers seek to identify its origin and purpose. Knowing whether new malware is from a known family or a previously unobserved family aids the efficiency of reverse engineers. This article presents a three‐stage method to classify new malware into a family by comparing its similarity to existing static traces, and assigning it to the most similar family. First, a fast filtering method creates a shortlist of samples with some similarity to the new malware, using a simple bigram comparison of the instructions. The second stage takes the call graph view of the shortlisted static traces and uses simulated annealing to estimate the graph edit distance, a measure of dissimilarity between graphs. Finally, a random forest classifier combines the previous two results to predict the family to which a new sample belongs. The paper also considers how to detect when malware is from a new family.

Improving data exploration in graphs with fuzzy logic and large-scale visualisation

This work presents three case-studies of how fuzzy logic can be combined with large-scale immersive visualisation to enhance the process of graph sensemaking, enabling interactive fuzzy filtering of large global views of graphs. The aim is to provide users a mechanism to quickly identify interesting nodes for further analysis. Fuzzy logic allows a flexible framework to ask human-like curiosity-driven questions over the data, and visualisation allows its communication and understanding. Together, these two technologies successfully empower novices and experts to a faster and deeper understanding of the underlying patterns in big datasets compared to traditional means in a desktop screen with crisp queries. Among other examples, we provide evidence of how these two technologies successfully enable the identification of relevant transaction patterns in the Bitcoin network.

VisMatchmaker: Cooperation of the User and the Computer in Centralized Matching Adjustment.

Centralized matching is a ubiquitous resource allocation problem. In a centralized matching problem, each agent has a preference list ranking the other agents and a central planner is responsible for matching the agents manually or with an algorithm. While algorithms can find a matching which optimizes some performance metrics, they are used as a black box and preclude the central planner from applying his domain knowledge to find a matching which aligns better with the user tasks. Furthermore, the existing matching visualization techniques (i.e. bipartite graph and adjacency matrix) fail in helping the central planner understand the differences between matchings. In this paper, we present VisMatchmaker, a visualization system which allows the central planner to explore alternatives to an algorithm-generated matching. We identified three common tasks in the process of matching adjustment: problem detection, matching recommendation and matching evaluation. We classified matching comparison into three levels and designed visualization techniques for them, including the number line view and the stacked graph view. Two types of algorithmic support, namely direct assignment and range search, and their interactive operations are also provided to enable the user to apply his domain knowledge in matching adjustment.

ETGraph: A graph-based approach for visual analytics of eye-tracking data

Mind wander(ing) (MW) or zoning out is a ubiquitous phenomenon where attention involuntary shifts from task-related processing to task-unrelated thoughts. Unfortunately, MW is a highly internal state so it cannot be readily inferred from overt behaviors and expressions. To help experts investigate mind wanderings, we present a graph-based approach for visual analytics of eye-tracking data, which utilizes the graph representations to illustrate the reading patterns and further help experts detect and verify mind wanderings based on the graph structures and other graph attributes. The input data are collected from multiple participants reading multiple pages of a book on a computer screen. Our approach first clusters fixations into fixation clusters, then creates the eye-tracking graph, i.e., ETGraph, for use in conjunction with the standard page view, time view, and statistics view. The graph view presents a visual representation of the actual reading patterns of a single participant or multiple participants and therefore serves as the main visual interface for exploration and navigation. We design a suite of techniques to help users identify common reading patterns and outliers for analytical reasoning at three different levels of detail: single participant single page, single participant multiple pages, and multiple participants single page. Interactive querying and filtering functions are provided for reducing visual clutter in the visualization and enabling users to answer questions and glean insights. Our tool also facilitates the detection and verification of mind wandering that the experts seek to investigate. We conduct a user study and an expert evaluation to assess the effectiveness of ETGraph in terms of its visual summarization and comparison capabilities.

Exploring and Comparing Clusterings of Multivariate Data Sets Using Persistent Homology

AbstractClustering algorithms support exploratory data analysis by grouping inputs that share similar features. Especially the clustering of unlabelled data is said to be a fiendishly difficult problem, because users not only have to choose a suitable clustering algorithm but also a suitable number of clusters. The known issues of existing clustering validity measures comprise instabilities in the presence of noise and restrictive assumptions about cluster shapes. In addition, they cannot evaluate individual clusters locally. We present a new measure for assessing and comparing different clusterings both on a global and on a local level. Our measure is based on the topological method of persistent homology, which is stable and unbiased towards cluster shapes. Based on our measure, we also describe a new visualization that displays similarities between different clusterings (using a global graph view) and supports their comparison on the individual cluster level (using a local glyph view). We demonstrate how our visualization helps detect different—but equally valid—clusterings of data sets from multiple application domains.

BIOMedical Search Engine Framework: Lightweight and customized implementation of domain-specific biomedical search engines

Background and objectivesText mining and semantic analysis approaches can be applied to the construction of biomedical domain-specific search engines and provide an attractive alternative to create personalized and enhanced search experiences. Therefore, this work introduces the new open-source BIOMedical Search Engine Framework for the fast and lightweight development of domain-specific search engines. The rationale behind this framework is to incorporate core features typically available in search engine frameworks with flexible and extensible technologies to retrieve biomedical documents, annotate meaningful domain concepts, and develop highly customized Web search interfaces. MethodsThe BIOMedical Search Engine Framework integrates taggers for major biomedical concepts, such as diseases, drugs, genes, proteins, compounds and organisms, and enables the use of domain-specific controlled vocabulary. Technologies from the Typesafe Reactive Platform, the AngularJS JavaScript framework and the Bootstrap HTML/CSS framework support the customization of the domain-oriented search application. Moreover, the RESTful API of the BIOMedical Search Engine Framework allows the integration of the search engine into existing systems or a complete web interface personalization. ResultsThe construction of the Smart Drug Search is described as proof-of-concept of the BIOMedical Search Engine Framework. This public search engine catalogs scientific literature about antimicrobial resistance, microbial virulence and topics alike. The keyword-based queries of the users are transformed into concepts and search results are presented and ranked accordingly. The semantic graph view portraits all the concepts found in the results, and the researcher may look into the relevance of different concepts, the strength of direct relations, and non-trivial, indirect relations. The number of occurrences of the concept shows its importance to the query, and the frequency of concept co-occurrence is indicative of biological relations meaningful to that particular scope of research. Conversely, indirect concept associations, i.e. concepts related by other intermediary concepts, can be useful to integrate information from different studies and look into non-trivial relations. ConclusionsThe BIOMedical Search Engine Framework supports the development of domain-specific search engines. The key strengths of the framework are modularity and extensibilityin terms of software design, the use of open-source consolidated Web technologies, and the ability to integrate any number of biomedical text mining tools and information resources. Currently, the Smart Drug Search keeps over 1,186,000 documents, containing more than 11,854,000 annotations for 77,200 different concepts. The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef.

Effects of Organic Additives on Formation of Copper Pillar during Electroplating Process

Cu electroplating process has been extensively used in the fabrication of on-chip interconnects such as Cu pillar, TSV, micro-bump of microelectronic devices for their further integration [1]. In general, the process is performed in a acid plating bath made up of inorganic components (copper sulfate, chloride ion, and sulfuric acid) and organic additives including suppressor, accelerator, and leveler. The organic additives play very critical roles in changing the shape, uniformity of deposits [2]. In the case of Cu pillar for flip chip packaging, a flat surface on the top of Cu pillar is required for implementation of the stacking materials like Sn-Ag solder [3]. In this study, Cu pillar with flat surface was successfully electroplated onto photoresist(PR) patterned Si wafer by controlling composition of the three organic additives. In order to understand the correlation between characteristics of additives (functional group, molar mass, N density and so on) and deposits, electrochemical behavior, PR reactivity and shape of Cu pillar have been investigated by galvanostatic potential transient measurements, contact angle measurement and scanning electron microscopy, respectively. As a result of the investigation, we will identify effects of the additives on formation of Cu pillar during electroplating process, and apply electroplating technology to many part of electronic devices from wide pillar to fine-pitch micro-bump. Fig. 1. SEM images of (a) cross-section view and (b) tilted view of Cu pillar (100 um in diameter and 40um in height) and (c) chronopotentiometry graph (inner image: confocal laser scanning microscopic graph and OM image of top view) [1] P.C. Andricacos, The Electrochemical Society Interface, spring (1999) 32-37. [2] W.C. Chen, T.T. Huang, C.H. Lai, S.W. Chou, C.H. Shih, ECS Transactions, 52 (2013) 453-460. [3] S. Chung, E. Kuo, M. Tseng, IMPACT 2009. 4th International. IEEE, (2009) 432-435. Figure 1

The Visual Causality Analyst: An Interactive Interface for Causal Reasoning.

Uncovering the causal relations that exist among variables in multivariate datasets is one of the ultimate goals in data analytics. Causation is related to correlation but correlation does not imply causation. While a number of casual discovery algorithms have been devised that eliminate spurious correlations from a network, there are no guarantees that all of the inferred causations are indeed true. Hence, bringing a domain expert into the casual reasoning loop can be of great benefit in identifying erroneous casual relationships suggested by the discovery algorithm. To address this need we present the Visual Causal Analyst-a novel visual causal reasoning framework that allows users to apply their expertise, verify and edit causal links, and collaborate with the causal discovery algorithm to identify a valid causal network. Its interface consists of both an interactive 2D graph view and a numerical presentation of salient statistical parameters, such as regression coefficients, p-values, and others. Both help users in gaining a good understanding of the landscape of causal structures particularly when the number of variables is large. Our framework is also novel in that it can handle both numerical and categorical variables within one unified model and return plausible results. We demonstrate its use via a set of case studies using multiple practical datasets.

Mining Graphs for Understanding Time-Varying Volumetric Data.

A notable recent trend in time-varying volumetric data analysis and visualization is to extract data relationships and represent them in a low-dimensional abstract graph view for visual understanding and making connections to the underlying data. Nevertheless, the ever-growing size and complexity of data demands novel techniques that go beyond standard brushing and linking to allow significant reduction of cognition overhead and interaction cost. In this paper, we present a mining approach that automatically extracts meaningful features from a graph-based representation for exploring time-varying volumetric data. This is achieved through the utilization of a series of graph analysis techniques including graph simplification, community detection, and visual recommendation. We investigate the most important transition relationships for time-varying data and evaluate our solution with several time-varying data sets of different sizes and characteristics. For gaining insights from the data, we show that our solution is more efficient and effective than simply asking users to extract relationships via standard interaction techniques, especially when the data set is large and the relationships are complex. We also collect expert feedback to confirm the usefulness of our approach.

TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data

We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis.