Randomization and approximation techniques in computer science

Selecting keywords from Twitter as features to identify events is challenging due to language informality such as acronyms, misspelled words, synonyms, transliteration and ambiguous terms. In this paper, We compare and identify the best methods for keyword selection as features to be used for classification purposes. Specifically, we study the aspects affecting keywords as features to identify civil unrest and protests. These aspects include the word count, the word forms such as n-gram, skip-gram and bags-of-words as well as the data association methods including correlation techniques and similarity techniques. To test the impact of the mentioned factors, we developed a framework that analyzed 641 days of tweets and extracted the words highly associated with event days along the same time frame. Then, we used the extracted words as features to classify any single day to be either an event day or a nonevent day in a specific location. In this framework, we used the same pipeline of data cleaning, prepossessing, feature selection, model learning and event classification using all combinations of keyword selection criteria. We used Naive Bayes classifier to learn the selected features and accordingly predict the event days. The classification is tested using multiple metrics, such as accuracy, precision, recall, F-score and AUC. This study concluded that the best word form is bag-of-words with average AUC of 0.72 and the best word count is two with average AUC of 0.74 and the best feature selection method is Spearman's correlation with average AUC of 0.89 and the best classifier for event detection is Naive Bayes Classifier.

Feature selection methods for event detection in Twitter: a text mining approach

Methods to find clusters in a network have been studied extensively because clustering has practical importance in many applications. Commonly used methods include spectral clustering and Newman’s modularity maximization. However, there has been no unified view of the two methods. In this study, we introduce an innovative guiding principle based on correspondence analysis to obtain node coordinates and discuss its equivalence to spectral clustering and Newman’s modularity. Besides, we discuss a degeneration case and its significance.

Correspondence analysis-based network clustering and importance of degenerate solutions unification of spectral clustering and modularity maximization

Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain con-nectome, population ﬂows and messages exchanges. In this work, we consider dynamic networks that are temporal sequences of graph snapshots, and aim at detecting abrupt changes in their structure. This task is often termed network change-point detection and has numerous applications, such as fraud detection or physical motion monitoring. Leveraging a graph neural network model, we design a method to perform online network change-point detection that can adapt to the spe-ciﬁc network domain and localise changes with no delay. The main novelty of our method is to use a siamese graph neural network architecture for learning a data-driven graph similarity function, which allows to e ﬀ ectively compare the current graph and its recent history. Importantly, our method does not require prior knowledge on the network generative distribution and is agnostic to the type of change-points; moreover, it can be applied to a large variety of networks, that include for instance edge weights and node attributes. We show on synthetic and real data that our method enjoys a number of beneﬁts: it is able to learn an adequate graph similarity function for performing online network change-point detection in diverse types of change-point settings, and requires a shorter data history to detect changes than most existing state-of-the-art baselines.

Graph similarity learning for change-point detection in dynamic networks

A framework to preserve distance-based graph properties in network embedding

Change detection in dynamic networks is an important problem in many areas, such as fraud detection, cyber intrusion detection and healthcare monitoring. It is a challenging problem because it involves a time sequence of graphs, each of which is usually very large and sparse with heterogeneous vertex degrees, resulting in a complex,
 high-dimensional mathematical object. Spectral embedding methods provide an effective way to transform a graph to a lower dimensional latent Euclidean space that preserves the underlying structure of the network. Although change detection methods that use spectral embedding are available, they do not address sparsity and degree heterogeneity that usually occur in noisy real-world graphs and a majority of these methods focus on changes in the behaviour of the overall network. In this paper, we adapt previously developed techniques in spectral graph theory and propose a novel concept of applying Procrustes techniques to embedded points for vertices in a graph to detect changes in entity behaviour. Our spectral embedding approach not only addresses sparsity and degree heterogeneity issues, but also obtains an estimate of the appropriate embedding dimension. We call this method CDP (change detection using Procrustes analysis). We demonstrate the performance of CDP through extensive simulation experiments and a real-world application. CDP successfully detects various types of vertex-based changes including (1) changes in vertex degree, (2) changes in community membership of vertices, and (3) unusual increase or decrease in edge weights between vertices. The change detection performance of CDP is compared with two other baseline methods that employ alternative spectral embedding approaches. In both cases, CDP generally shows superior performance.

Change detection in noisy dynamic networks: a spectral embedding approach

Change detection in dynamic networks is an important problem in many areas, such as fraud detection, cyber intrusion detection and health care monitoring. It is a challenging problem because it involves a time sequence of graphs, each of which is usually very large and sparse with heterogeneous vertex degrees, resulting in a complex, high dimensional mathematical object. Spectral embedding methods provide an effective way to transform a graph to a lower dimensional latent Euclidean space that preserves the underlying structure of the network. Although change detection methods that use spectral embedding are available, they do not address sparsity and degree heterogeneity that usually occur in noisy real-world graphs and a majority of these methods focus on changes in the behaviour of the overall network. 
In this paper, we adapt previously developed techniques in spectral graph theory and propose a novel concept of applying Procrustes techniques to embedded points for vertices in a graph to detect changes in entity behaviour. Our spectral embedding approach not only addresses sparsity and degree heterogeneity issues, but also obtains an estimate of the appropriate embedding dimension. We call this method CDP (change detection using Procrustes analysis). We demonstrate the performance of CDP through extensive simulation experiments and a real-world application. CDP successfully detects various types of vertex-based changes including (i) changes in vertex degree, (ii) changes in community membership of vertices, and (iii) unusual increase or decrease in edge weight between vertices. The change detection performance of CDP is compared with two other baseline methods that employ alternative spectral embedding approaches. In both cases, CDP generally shows superior performance.

Change Detection in Noisy Dynamic Networks: A Spectral Embedding Approach

Since the early civilizations, transportation has played a significant role, from fulfilling basic human needs to contributing towards major economic growths all over the world. With the advancement in technology, the demand for smooth and hassle-free transportation increased and it is particularly true for road transportation in Sri Lanka as well. As a result, the expressway road network was introduced to Sri Lanka in 2011. Although a toll is payable for the use of expressways, many vehicle users prefer to utilize the expressway due to the extensive amount of time saved. Time is of utmost importance for expressway users. Hence, long queues and waiting time at toll plazas where the toll payment is made should be minimized. This study is aimed at analyzing the performance at the Peliyagoda toll plaza of the Colombo-Katunayake expressway where the formation of long queues and long waiting time in queues can be observed during peak hours. Due to the high complexity of using the analytical approach in obtaining the performance measures, a simulation approach was used with Arena Simulation Software. Few setup improvements were identified, and each of the setups were simulated to obtain the performance measures. Based on the comparison of the results, recommendations and suggestions to improve the efficiency of the operations at the Peliyagoda toll plaza have been outlined.

Isuru Udayangani Hewapathirana

Papers

Change detection in noisy dynamic networks: a spectral embedding approach

Change Detection in Noisy Dynamic Networks: A Spectral Embedding Approach

Simulation analysis of an expressway toll plaza