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Input temporal network used in the second experiment. Figures generated in Python (Python Software Foundation. Python Language Reference. Available at http://www.python.org).
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Temporal network mining tasks are usually hard problems. This is because we need to face not only a large amount of data but also its non-stationary nature. In this paper, we propose a method for temporal network pattern representation and pattern change detection following the reductionist approach. The main idea is to model each stable (durable)...
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... second example intends to show how the proposed method represents and detects periodic patterns of a temporal network. The input network consists of 10 (Fig. 7). In total, we have 60 networks in 3 states. Again, only 5 nodes are selected for each state network. Figure 8(a) shows the target network and we see the three correctly detected communities. www.nature.com/scientificreports www.nature.com/scientificreports/ Figure 8(b) shows that the localized average domination level reaches its ...
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... Such as, the algorithm MLI based on network embedding and mechanism learning were proposed to identify critical nodes in temporal networks and experimental results show that the proposed method outperform these well-known methods in identifying critical nodes under spreading dynamic 30 . Similarly, focused on the identification of key edges in temporal networks, they proposed a method for identifying the minimum number of edges that need to be moved in the scenario where the epidemic spreading range is minimized in temporal networks [31][32][33] . ...
In the new media environment, the constantly emerging social platforms further expand the channels for the propagation of public opinion. Under the framework of complex network theory and faced the needs of management practice, modeling the spreading dynamics of public opinion in temporal group networks is of great significance for understanding its spreading law and improving the governance system of cyberspace and the development of network science. Through analyzing the changes of group networks topology and the spreading rules of public opinion, the spreading model of public opinion in temporal group networks was proposed by coupling the two dynamic processes, and the spreading thresholds of public opinion in static and temporal group networks were derived respectively. Then, the spreading characteristics of public opinion under different network topology, as well as the influence of important parameters on public opinion spreading process were discussed with the help of simulation experiments. The research results indicated that the propagation of public opinion in static and temporal group networks exhibits both similar trends and differentiated characteristics; compared with Spreader, the propagation of public opinion in temporal group networks is more sensitive to Ignorant’s behavior; both groups’ and netizens’ active probability have significant influence on public opinion propagation, but netizens’ affects more. Based on the relevant results, this paper proposed a series of countermeasures such as, grading social platforms, strengthening relationship management between them and introducing time management systems, so as to promote the formation of a good network ecosystem and the modernization of the national governance system.
... A salient feature of the foregoing examples is the development and evolution of their underlying structure, as characterized by change in their properties over time [27]. Temporal networks incorporate nodes and/or edges that are encoded with time-based information (e.g., a timestamp or time window) [28,29]. Thus, given the temporal characteristics of a node and/or edge, the timing for their inclusion within a network are explicitly determined. ...
In this research various concepts from network theory and topic modelling are combined, to provision a temporal network of associated topics. This solution is presented as a step-by-step process to facilitate the evaluation of latent topics from unstructured text, as well as the domain area that textual documents are sourced from. In addition to ensuring shifts and changes in the structural properties of a given corpus are visible, non-stationary classes of cooccurring topics are determined, and trends in topic prevalence, positioning, and association patterns are evaluated over time. The aforementioned capabilities extend the insights fostered from stand-alone topic modelling outputs, by ensuring latent topics are not only identified and summarized, but more systematically interpreted, analysed, and explained, in a transparent and reliable way.
... Community evolution tracking is a method that studies group activities in social networks and has formed a relatively mature theoretical framework [15]. Meanwhile, it is a basic approach for studying the evolution of temporal networks [5,10,19]. Independent community detection and matching is the most common type of event-based community evolution tracking method. This type of technique usually consists of two steps. ...
... Forming: The forming of a community means that it appears in the current snapshot and has not appeared in the previous snapshot: (15) Dissolving: The dissolving of a community means that it appeared in the previous snapshot and is not appear in the current snapshot: (16) Continuing: The continuing of a community means that the size of it in two adjacent snapshots is the same, with only minor changes in members: (17) Growing: The growing of a community refers to the addition of other members who do not belong to the community when the initial members of it remain unchanged: (18) Shrinking: The shrinking of a community means that some members leave the community when the initial members of it remain unchanged: (19) Merging: The merging means that members in multiple community members in the current snapshot gather to form one community in the next snapshot: (20) Splitting: The splitting means that members of a community in the current snapshot form multiple communities in the next snapshot. (21) and ...
The social network is closely related to people’s lives. And social events are the products of the human subjective initiative during the evolution of networks. Therefore, there is a close correlation between social events and network evolution. This paper studies the characteristics of network evolution corresponding to social events from the perspective of temporal networks. The change point detection method is applied to capture the “shocks” of social events on the network structure. Then, the patterns of structural changes are analyzed based on the theory of community evolution. Experiments on two cases illustrate that social events are significant milestones to promote the development of social networks. And the mesostructure is the intermediary connecting evolving network and social events.
... It has advanced over the last decade with the developments of techniques tackling the time series in different domains [1,2]. An important area of research is analyzing the time series with the aid of a complex network [3][4][5][6][7][8][9][10][11][12][13]. Studies on network topology have remarkably advanced our understanding of this domain, both in terms of data complexity-that went from a single univariate time series to complex data flows containing concept drifts [3][4][5][6]-and in terms of proposed frameworks [13][14][15][16][17]28,29]. ...
... An important area of research is analyzing the time series with the aid of a complex network [3][4][5][6][7][8][9][10][11][12][13]. Studies on network topology have remarkably advanced our understanding of this domain, both in terms of data complexity-that went from a single univariate time series to complex data flows containing concept drifts [3][4][5][6]-and in terms of proposed frameworks [13][14][15][16][17]28,29]. Most of these frameworks can be generalized by a two-step process of converting the data into a network (mapping) and analyzing it through network metrics. ...
... In particular, detecting the community structure of the network has gained a lot of attention since some works have shown that the community structure can represent the data patterns and reflect changes along time [17][18][19][20]. More recently, some works have shown that, intuitively, the communities can contain information about structural patterns of the original data [5,13], even exploring repetition cycles for stochastic times series [29]. Although previous works have set well-established tools for moda e-mail: anghinoni@usp.br ...
Identifying time series patterns is of great importance for many real-world problems in a variety of scientific fields. Here, we present a method to identify time series patterns in multiscale levels based on the hierarchical community representation in a complex network. The construction method transforms the time series into a network according to its segments’ correlation. The constructed network’s quality is evaluated in terms of the largest correlation threshold that reaches the largest main component’s size. The presence of repeated hierarchical patterns is then captured through network metrics, such as the modularity along the community detection process. We show the benefits of the proposed method by testing in one artificial dataset and two real-world time series applications. The results indicate that the method can successfully identify the original data’s hierarchical (micro and macro) characteristics.
... An alternative approach to using the dissipation index can in-clude temporal community detection [40]; however, this method is not necessarily suitable for the data-scarce regime with only two time points considered in this work. The indication that the post-hoc analysis of communities is more suitable for analysis was also recently demonstrated when studying fire events in a portion of the Amazon basin [41]. ...
Understanding temporal biological phenomena is a challenging task that can be approached using network analysis. Here, we explored whether network reconstruction can be used to better understand the temporal dynamics of bois noir, which is associated with ‘Candidatus Phytoplasma solani’, and is one of the most widespread phytoplasma diseases of grapevine in Europe. We proposed a methodology that explores the temporal network dynamics at the community level, i.e., densely connected subnetworks. The methodology offers both insights into the functional dynamics via enrichment analysis at the community level, and analyses of the community dissipation, as a measure that accounts for community degradation. We validated this methodology with cases on experimental temporal expression data of uninfected grapevines and grapevines infected with ‘Ca. P. solani’. These data confirm some known gene communities involved in this infection. They also reveal several new gene communities and their potential regulatory networks that have not been linked to ‘Ca. P. solani’ to date. To confirm the capabilities of the proposed method, selected predictions were empirically evaluated.
... par un graphe statique les interactions qui y ont lieu [44,22,48]. L'évolution du système complexe devient une séquence ordonnée de graphes représentant l'ensemble des arêtes qui apparaissent dans un intervalle de temps donné. ...
... Il existe dans la littérature plusieurs formalismes pour représenter les graphes dynamiques. Le plus classique est la représentation sous forme de séquence de graphes statiques [44,22,48]. Chaque élément de la séquence représente alors un cliché (snapshot) de l'état du graphe dynamique dans une fenêtre de temps. ...
... L'étude de la topologie des systèmes complexes a permis la définition d'un ensemble de composantes primaires communes à de nombreuses propriétés et applications [62]. Parmi les composantes les plus représentatives dans le domaine, on distingue les cliques [84,44]. Ce sont des sous-graphes fortement connexes qui s'apparentent, par exemple, aux communautés dans les réseaux sociaux. ...
L'utilisation toujours plus importante de l'informatique et d'internet mène à une génération toujours plus importante de donnés et de communications. Ces données peuvent être par exemple des historiques de communication dans des réseaux sociaux ou encore des traces du trafic internet. De tels historiques de communications sont une forme de graphe dynamique formalisable par des flots de liens. De nombreux travaux s'articulent autour de la supervision et l'analyse de ces systèmes afin de détecter l'apparition de certains phénomènes ou scénarios spécifiques. Par exemple, dans le cadre de la sécurité, on souhaite détecter des tentatives d'intrusions concertées, tel des DDOS. Une première problématique est la création d'un langage général et normalisé de spécification de tel phénomène, car peu de langages existent et ils sont souvent spécifiques à certaines catégories de scénarios par soucis de performance. La seconde problématique principale est l'implémentation d'un prototype d'outil de reconnaissance pour détecter ces phénomènes dans des jeux de données issues de situation réelles.Notre approche consiste à représenter ces propriétés comme des motifs spécifiés avec un dérivé des expressions régulières: les expressions temporisées à couches mémoire. Ces expressions permettent à la fois de spécifier des contraintes de temps, et donc de représenter le dynamisme des systèmes, mais aussi de représenter des données d'un environnement ouvert. Ce dernier représente le fait que les entités/acteurs présents dans les réseaux étudiés ne sont pas toujours connues à l'avance et peuvent apparaitre ou disparaitre au cours de son évolution. Dans le cas de la détection d'intrusion, l'on ne peut pas connaitre les identités des attaquants à l'avance. Comme pour les expressions régulières, la théorie des automates offre des formalismes de reconnaissance pour les différents types de propriétés caractérisant les motifs. Le modèle des automates temporisés, est classique dans la littérature pour sa capacité à formaliser des contraintes de temps. De plus, les différentes classes d'automates à mémoire permettant la reconnaissance de langage sur des alphabets infinis, correspondant à des motifs sur des environnements ouverts. Nous avons conçu le modèle des automates temporisés à couches mémoire, intégrant les caractéristiques de ces deux catégories d'automates. L'une des caractéristiques de ce modèle est l'introduction de la notion de couches mémoire offrant une flexibilité quant à la définition de propriétés complexes. Nous prouvons l'équivalence entre cette classe d'automate et les expressions temporisées à couches mémoire avec un théorème semblable au théorème de Kleene. Cela nous permet de précisément caractériser, et positionner dans la littérature, la classe des motifs exprimés.Enfin, la dernière contribution de cette thèse est le développement et l'implémentation d'un algorithme de reconnaissance générique. Son implémentation dans un outil nous permet d'effectuer des expérimentations sur des flots de liens issus de réseaux réels. Nous avons ainsi pu modéliser des scénarios d'intrusion dans des réseaux, et appliquer notre outil au problème de la détection de communautés dans les réseaux sociaux.
... In this work, we aim to analyze the temporal information stored on the wildfire dataset by using temporal chronnets. Our analysis differs from previous works [2,13,14] once we seek to validate the results of the temporal community detection methods when modeling the wildfires, mainly concerning its spatial incidence and extension. Our methodology can reveal where and how often specific fire event patterns occur over the years. ...
... Recently, Gao et al. [14] proposed a method for mining temporal networks by representative nodes in a community identification approach. They tackled the problem from the change detection perspective, where each stable temporal state of the dataset is represented as a community. ...
... The authors showed the effectivity of the proposed method in some artificial datasets, and in a case study analyzing wildfire events from the same temporal Chronnets dataset [2,13]. As a result, they also detected two central communities in the Amazon region, each one corresponding to different periods of the year: the south-hemisphere winter season, with a high tendency of fires, and the south-hemisphere summer season, with a low frequency of fires [14]. However, these communities represent the global state of the wildfire system in the Amazon basin, not the micro spatialtemporal particularities or patterns into the microregions. ...
The study and comprehension of complex systems are crucial intellectual and scientific challenges of the 21st century. In this scenario, network science has emerged as a mathematical tool to support the study of such systems. Examples include environmental processes such as wildfires, which are known for their considerable impact on human life. However, there is a considerable lack of studies of wildfire from a network science perspective. Here, employing the chronological network concept -- a temporal network where nodes are linked if two consecutive events occur between them -- we investigate the information that dynamic community structures reveal about the wildfires' dynamics. Particularly, we explore a two-phase dynamic community detection approach, i.e., we applied the Louvain algorithm on a series of snapshots. Then we used the Jaccard similarity coefficient to match communities across adjacent snapshots. Experiments with the MODIS dataset of fire events in the Amazon basing were conducted. Our results show that the dynamic communities can reveal wildfire patterns observed throughout the year.
... In this sense, it is not possible to evaluate the performance and role of the nodes, nor understanding the interaction patterns into the network. As alternative, a temporal network (G) can be represented as an ordered sequence of network observations at different time-steps or intervals [14], i.e., G = {G 0 , G 1 , . . . , G l } with l the number of layers or snapshots. ...
Chat groups are well-known for their capacity to promote viral political and marketing campaigns, spread fake news, and create rallies by hundreds of thousands on the streets. With the increasing public awareness regarding privacy and surveillance, many platforms have started to deploy end-to-end encrypted protocols. In this context, the group’s conversations are not accessible in plain text or readable format by third-party organizations or even the platform owner. Then, the main challenge that emerges is related to getting insights from users’ activity of those groups, but without accessing the messages. Previous approaches evaluated the user engagement by assessing user’s activity, however, on limited conditions where the data is encrypted, they cannot be applied. In this work, we present a framework for measuring the level of engagement of group conversations and users, without reading the messages. Our framework creates an ensemble of interaction networks that represent the temporal evolution of the conversation, then, we apply the proposed Engagement Index (EI) for each interval of conversations to assess users’ participation. Our results in five datasets from real-world WhatsApp Groups indicate that, based on the EI, it is possible to identify the most engaged users within a time interval, create rankings, and group users according to their engagement and monitor their performance over time.
... In this work, we aim to analyze the temporal information stored on the wildfire dataset by using temporal chronnets. Our analysis differs from previous works [2,13,14] once we seek to validate the results of the temporal community detection methods when modeling the wildfires, mainly concerning its spatial incidence and extension. Our methodology can reveal where and how often specific fire event patterns occur over the years. ...
... Recently, Gao et al. [14] proposed a method for mining temporal networks by representative nodes in a community identification approach. They tackled the problem from the change detection perspective, where each stable temporal state of the dataset is represented as a community. ...
... The authors showed the effectivity of the proposed method in some artificial datasets, and in a case study analyzing wildfire events from the same temporal Chronnets dataset [2,13]. As a result, they also detected two central communities in the Amazon region, each one corresponding to different periods of the year: the south-hemisphere winter season, with a high tendency of fires, and the south-hemisphere summer season, with a low frequency of fires [14]. However, these communities represent the global state of the wildfire system in the Amazon basin, not the micro spatialtemporal particularities or patterns into the microregions. ...
The study and comprehension of complex systems are crucial intellectual and scientific challenges of the 21st century. In this scenario, network science has emerged as a mathematical tool to support the study of such systems. Examples include environmental processes such as the wildfires, which are known for their considerable impact on human life. However, there is a considerable lack of studies of wildfire from a network science perspective. Here, employing the chronological network concept—a temporal network where nodes are linked if two consecutive events occur between them—we investigate the information that dynamic community structures reveal about the wildfires’ dynamics. Particularly, we explore a two-phase dynamic community detection approach, i.e., we applied the Louvain algorithm on a series of snapshots, and then we used the Jaccard similarity coefficient to match communities across adjacent snapshots. Experiments with the MODIS dataset of fire events in the Amazon basing were conducted. Our results show that the dynamic communities can reveal wildfire patterns observed throughout the year.
