Time-topology analysis

2021 ◽  
Vol 14 (13) ◽  
pp. 3322-3334
Author(s):  
Yunkai Lou ◽  
Chaokun Wang ◽  
Tiankai Gu ◽  
Hao Feng ◽  
Jun Chen ◽  
...  
Keyword(s):  
Temporal Information ◽  
Time Dimension ◽  
Topology Analysis ◽  
Topological Information ◽  
Analysis Methods ◽  
Short Period ◽  
Pruning Strategy ◽  
Temporal Graph ◽  
Types Of Information ◽  
Temporal Graphs

Many real-world networks have been evolving, and are finely modeled as temporal graphs from the viewpoint of the graph theory. A temporal graph is informative, and always contains two types of information, i.e., the temporal information and topological information, where the temporal information reflects the time when the relationships are established, and the topological information focuses on the structure of the graph. In this paper, we perform time-topology analysis on temporal graphs to extract useful information. Firstly, a new metric named T-cohesiveness is proposed to evaluate the cohesiveness of a temporal subgraph. It defines the cohesiveness of a temporal subgraph from the time and topology dimensions jointly. Specifically, given a temporal graph G s = ( Vs , ε Es ), cohesiveness in the time dimension reflects whether the connections in G s happen in a short period of time, while cohesiveness in the topology dimension indicates whether the vertices in V s are densely connected and have few connections with vertices out of G s . Then, T-cohesiveness is utilized to perform time-topology analysis on temporal graphs, and two time-topology analysis methods are proposed. In detail, T-cohesiveness evolution tracking traces the evolution of the T-cohesiveness of a subgraph, and combo searching finds out all the subgraphs that contain the query vertex and have T-cohesiveness larger than a given threshold. Moreover, a pruning strategy is proposed to improve the efficiency of combo searching. Experimental results confirm the efficiency of the proposed time-topology analysis methods and the pruning strategy.

A two-stream network with joint spatial-temporal distance for video-based person re-identification

2020 ◽  
Vol 39 (3) ◽  
pp. 3769-3781
Author(s):  
Zhisong Han ◽  
Yaling Liang ◽  
Zengqun Chen ◽  
Zhiheng Zhou
Keyword(s):  
Spatial Information ◽  
Temporal Information ◽  
Temporal Distance ◽  
Spatial Distance ◽  
Weighted Sum ◽  
Stream Network ◽  
Spatial Features ◽  
Public Datasets ◽  
Types Of Information ◽  
Temporal Information Extraction

Video-based person re-identification aims to match videos of pedestrians captured by non-overlapping cameras. Video provides spatial information and temporal information. However, most existing methods do not combine these two types of information well and ignore that they are of different importance in most cases. To address the above issues, we propose a two-stream network with a joint distance metric for measuring the similarity of two videos. The proposed two-stream network has several appealing properties. First, the spatial stream focuses on multiple parts of a person and outputs robust local spatial features. Second, a lightweight and effective temporal information extraction block is introduced in video-based person re-identification. In the inference stage, the distance of two videos is measured by the weighted sum of spatial distance and temporal distance. We conduct extensive experiments on four public datasets, i.e., MARS, PRID2011, iLIDS-VID and DukeMTMC-VideoReID to show that our proposed approach outperforms existing methods in video-based person re-ID.

scholarly journals Cognitive Task Analysis Methods in Envisioned Tactical Command Decision Making

2019 ◽  
Vol 63 (1) ◽  
pp. 262-266
Author(s):  
Nicolette M. McGeorge ◽  
Stephanie Kane ◽  
Chris Muller
Keyword(s):  
Decision Making ◽  
Situation Awareness ◽  
Task Analysis ◽  
Information Needs ◽  
Cognitive Task ◽  
Cognitive Task Analysis ◽  
Cyber Warfare ◽  
Cyber Attack ◽  
Analysis Methods ◽  
Types Of Information

The battlespace is a volatile and complex environment in which tactical commanders face cognitively challenging responsibilities, compounded with the increased complexity of emerging cyber warfare. It is critical that tactical commanders gain adequate situation awareness for effective decision making to achieve mission success. While current tools enable distribution of large quantities and types of information, they do not adequately support the underlying cognitive work and information needs of tactical commanders. We performed a domain analysis using Cognitive Task Analysis methods, developing a prototypical operational scenario representative of current and envisioned environments, centered on a cyber-attack. Using this analysis, we identified cognitive and information requirements for information displays that support effective tactical decision making. Tactical commanders need to understand dynamic situations in the field, understand the viable courses of actions, know how their mission fits into the larger mission, and communicate with their company subordinates and higher echelons of command.

scholarly journals 3-in-1 Correlated Embedding via Adaptive Exploration of the Structure and Semantic Subspaces

2018 ◽  
Author(s):  
Liang Yang ◽  
Yuanfang Guo ◽  
Di Jin ◽  
Huazhu Fu ◽  
Xiaochun Cao
Keyword(s):  
Real World ◽  
Posterior Distribution ◽  
State Of The Art ◽  
Variational Inference ◽  
Experimental Results ◽  
Generation Process ◽  
Network Embedding ◽  
Topological Information ◽  
Proposed Model ◽  
Types Of Information

Combinational  network embedding, which learns the node representation by exploring both  topological and non-topological information, becomes popular due to the fact that the two types of information are complementing each other.  Most of the existing methods either consider the  topological and non-topological  information being aligned or possess predetermined preferences during the embedding process.Unfortunately, previous methods  fail to either explicitly describe the correlations between topological and non-topological information or adaptively weight their impacts. To address the existing issues, three new assumptions are proposed to better describe the embedding space and its properties. With the proposed assumptions, nodes, communities and topics are mapped into one embedding space. A novel generative model is proposed to formulate the generation process of the network and content from the embeddings, with respect to the Bayesian framework. The proposed model automatically leans to the information which is more discriminative.The embedding result can be obtained by maximizing the posterior distribution by adopting the variational inference and reparameterization trick. Experimental results indicate that the proposed method gives superior performances compared to the state-of-the-art methods when a variety of real-world networks is analyzed.

scholarly journals Two Influence Maximization Games on Graphs Made Temporal

2021 ◽  
Author(s):  
Niclas Boehmer ◽  
Vincent Froese ◽  
Julia Henkel ◽  
Yvonne Lasars ◽  
Rolf Niedermeier ◽  
...  
Keyword(s):  
Nash Equilibria ◽  
Dynamic Nature ◽  
Games On Graphs ◽  
Graph Games ◽  
Graph Classes ◽  
Temporal Graph ◽  
Influence Spreading ◽  
Temporal Graphs ◽  
Competitive Diffusion ◽  
Over Time

To address the dynamic nature of real-world networks, we generalize competitive diffusion games and Voronoi games from static to temporal graphs, where edges may appear or disappear over time. This establishes a new direction of studies in the area of graph games, motivated by applications such as influence spreading. As a first step, we investigate the existence of Nash equilibria in competitive diffusion and Voronoi games on different temporal graph classes. Even when restricting our studies to temporal paths and cycles, this turns out to be a challenging undertaking, revealing significant differences between the two games in the temporal setting. Notably, both games are equivalent on static paths and cycles. Our two main technical results are (algorithmic) proofs for the existence of Nash equilibria in temporal competitive diffusion and temporal Voronoi games when the edges are restricted not to disappear over time.

scholarly journals Exploring longitudinal care histories for looked after children: a sequence analysis of administrative social care data

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Louise McGrath-Lone ◽  
Katie Harron ◽  
Lorraine Dearden ◽  
Ruth Gilbert
Keyword(s):  
Sequence Analysis ◽  
Administrative Data ◽  
Social Care ◽  
Term Care ◽  
Analysis Methods ◽  
Short Period ◽  
The Stability ◽  
Children In Care ◽  
Care History

BackgroundOutcomes for children in care vary by the stability of their placements (for example, more placement changes have been associated with poorer educational attainment). Official statistics describing the stability of care histories for children in England are limited to placement changes within a 12-month period. These annual statistical ‘snapshots’ cannot capture the complexity of children’s experiences; however, as administrative data have been routinely collected since 1992, it is possible to reconstruct longitudinal care histories. ObjectiveTo identify distinct patterns of care history by applying sequence analysis methods to longitudinal, administrative data. MethodsWe extracted care histories from birth to age 18 for a large, representative sample of children born 1992-94 (N=16,000) from routinely-collected Children Looked After Return data. We explored the heterogeneity of  children’s care histories in terms of stability and identified sub-groups based on the number, duration and timing of placements using sequence analysis methods. ResultsChildren’s care histories were varied with the number of placements ranging from 1 to 184 (median: 2). However, six distinct sub-groups of care history were evident including; adolescent entries (17.6%), long-term instability (13.1%) and early intervention (6.9%). Overall, most children (58.4%) had a care history that could be classified as’short-term care’ with an average of 276 days in care and 2.48 placements throughout childhood. Few children (4.0%) had a care history that could be described as ‘long-term stable care’. ConclusionsSequence analyses of longitudinal data can refine our understanding of how out-of-home care is used as a social care intervention. Despite the policy focus on achieving long-term stability for children in care, the vast majority of children remain in care for a short period of time. Future work exploring how outcomes vary between the different sub-groups of care history could enable better evaluation of the effects of longitudinal care experiences.

scholarly journals Distributed temporal graph analytics with GRADOOP

2021 ◽  
Author(s):  
Christopher Rost ◽  
Kevin Gomez ◽  
Matthias Täschner ◽  
Philip Fritzsche ◽  
Lucas Schons ◽  
...  
Keyword(s):  
Graph Model ◽  
Lessons Learned ◽  
Temporal Property ◽  
Graph Analytics ◽  
Matching Graph ◽  
Temporal Graph ◽  
Scalable Analysis ◽  
Temporal Graphs ◽  
Distributed Analytics ◽  
Difference Pattern

AbstractTemporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort.

scholarly journals Interference-free Walks in Time: Temporally Disjoint Paths

2021 ◽  
Author(s):  
Nina Klobas ◽  
George B. Mertzios ◽  
Hendrik Molter ◽  
Rolf Niedermeier ◽  
Philipp Zschoche
Keyword(s):  
Disjoint Paths ◽  
Constant Number ◽  
Underlying Graph ◽  
Traffic Routing ◽  
Temporal Graph ◽  
The One ◽  
Temporal Graphs ◽  
General Graphs ◽  
Temporal Path ◽  
Np Hardness

We investigate the computational complexity of finding temporally disjoint paths or walks in temporal graphs. There, the edge set changes over discrete time steps and a temporal path (resp. walk) uses edges that appear at monotonically increasing time steps. Two paths (or walks) are temporally disjoint if they never use the same vertex at the same time; otherwise, they interfere. This reflects applications in robotics, traffic routing, or finding safe pathways in dynamically changing networks. On the one extreme, we show that on general graphs the problem is computationally hard. The "walk version" is W[1]-hard when parameterized by the number of routes. However, it is polynomial-time solvable for any constant number of walks. The "path version" remains NP-hard even if we want to find only two temporally disjoint paths. On the other extreme, restricting the input temporal graph to have a path as underlying graph, quite counterintuitively, we find NP-hardness in general but also identify natural tractable cases.

Learning Temporal Information from Text

2011 ◽  
pp. 1146-1149 ◽  
Author(s):  
Feng Pan
Keyword(s):  
Natural Language ◽  
Intelligent Agents ◽  
Stock Price ◽  
Question Answering ◽  
Information Science ◽  
Video Retrieval ◽  
Temporal Information ◽  
Time Dimension ◽  
Temporal Expressions ◽  
Constraint Modeling

As an essential dimension of our information space, time plays a very important role in every aspect of our lives. Temporal information is necessarily required in many applications, such as temporal constraint modeling in intelligent agents (Hritcu and Buraga, 2005), semantic web services (Pan and Hobbs, 2004), temporal content modeling and annotation for semantic video retrieval (QasemiZadeh et al., 2006), geographic information science (Agarwal, 2005), data integration of historical stock price databases (Zhu et al., 2004), ubiquitous and pervasive systems for modeling the time dimension of the context (Chen et al., 2004), and so on. Extracting temporal information from text is especially useful for increasing the temporal awareness for different natural language applications, such as question answering, information retrieval, and summarization. For example, in summarizing a story in terms of a timeline, a system may have to extract and chronologically order events in which a particular person participated. In answering a question as to a person’s current occupation, a system may have to selectively determine which of several occupations reported for that person is the most recently reported one (Mani et al., 2004). This chapter presents recent advances in applying machine learning and data mining approaches to automatically extract explicit and implicit temporal information from natural language text. The extracted temporal information includes, for example, events, temporal expressions, temporal relations, (vague) event durations, event anchoring, and event orderings.

scholarly journals Comparing temporal graphs using dynamic time warping

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vincent Froese ◽  
Brijnesh Jain ◽  
Rolf Niedermeier ◽  
Malte Renken
Keyword(s):  
Real World ◽  
Dynamic Time Warping ◽  
Optimization Problem ◽  
Time Warping ◽  
Proximity Measure ◽  
Real World Data ◽  
Special Cases ◽  
Temporal Graph ◽  
Dynamic Time ◽  
Temporal Graphs

AbstractWithin many real-world networks, the links between pairs of nodes change over time. Thus, there has been a recent boom in studying temporal graphs. Recognizing patterns in temporal graphs requires a proximity measure to compare different temporal graphs. To this end, we propose to study dynamic time warping on temporal graphs. We define the dynamic temporal graph warping (dtgw) distance to determine the dissimilarity of two temporal graphs. Our novel measure is flexible and can be applied in various application domains. We show that computing the dtgw-distance is a challenging (in general) -hard optimization problem and identify some polynomial-time solvable special cases. Moreover, we develop a quadratic programming formulation and an efficient heuristic. In experiments on real-world data, we show that the heuristic performs very well and that our dtgw-distance performs favorably in de-anonymizing networks compared to other approaches.

scholarly journals Tracing Topic Transitions with Temporal Graph Clusters

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Xiaonan Jing ◽  
Qingyuan Hu ◽  
Yi Zhang ◽  
Julia Taylor Rayz
Keyword(s):  
Language Processing ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Twitter Data ◽  
Markov Clustering ◽  
Data Source ◽  
Temporal Graph ◽  
Node Removal ◽  
Temporal Graphs ◽  
Optimal Graph

Twitter serves as a data source for many Natural Language Processing (NLP) tasks. It can be challenging to identify topics on Twitter due to continuous updating data stream. In this paper, we present an unsupervised graph based framework to identify the evolution of sub-topics within two weeks of real-world Twitter data. We first employ a Markov Clustering Algorithm (MCL) with a node removal method to identify optimal graph clusters from temporal Graph-of-Words (GoW). Subsequently, we model the clustering transitions between the temporal graphs to identify the topic evolution. Finally, the transition flows generated from both computational approach and human annotations are compared to ensure the validity of our framework.

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