“…It is important to note that this method is independent from the choice of the algorithm used for the static community detection on the individual snapshots. Generally, the class of matching-based methods for temporal community detection [ 8 – 11 , 14 ] offers a big advantage, by allowing us to choose a static detection method for the specific data structure and question. Fig.…”
Section: Dynamics Of Communitiesmentioning
confidence: 99%
“…This naturally raises the question of a temporal matching of communities resulting from static snapshots [ 8 – 12 ]. By incorporating higher orders of memory [ 13 ] in a method proposed in [ 14 ], long-term developments can be tracked reliably.…”
Section: Introductionmentioning
confidence: 99%
“…Previously in [ 14 ], we introduced a random-walk approach for hashtag community detection and a subsequent memory-based matching scheme on temporal networks. We had demonstrated that the lifetime of small communities can be increased with this approach.…”
BackgroundHashtags are widely used for communication in online media. As a condensed version of information, they characterize topics and discussions. For their analysis, we apply methods from network science and propose novel tools for tracing their dynamics in time-dependent data. The observations are characterized by bursty behaviors in the increases and decreases of hashtag usage. These features can be reproduced with a novel model of dynamic rankings.Hashtag communities in timeWe build temporal and weighted co-occurrence networks from hashtags. On static snapshots, we infer the community structure using customized methods. On temporal networks, we solve the bipartite matching problem of detected communities at subsequent timesteps by taking into account higher-order memory. This results in a matching protocol that is robust toward temporal fluctuations and instabilities of the static community detection. The proposed methodology is broadly applicable and its outcomes reveal the temporal behavior of online topics.Modeling topic-dynamicsWe consider the size of the communities in time as a proxy for online popularity dynamics. We find that the distributions of gains and losses, as well as the interevent times are fat-tailed indicating occasional, but large and sudden changes in the usage of hashtags. Inspired by typical website designs, we propose a stochastic model that incorporates a ranking with respect to a time-dependent prestige score. This causes occasional cascades of rank shift events and reproduces the observations with good agreement. This offers an explanation for the observed dynamics, based on characteristic elements of online media.
“…It is important to note that this method is independent from the choice of the algorithm used for the static community detection on the individual snapshots. Generally, the class of matching-based methods for temporal community detection [ 8 – 11 , 14 ] offers a big advantage, by allowing us to choose a static detection method for the specific data structure and question. Fig.…”
Section: Dynamics Of Communitiesmentioning
confidence: 99%
“…This naturally raises the question of a temporal matching of communities resulting from static snapshots [ 8 – 12 ]. By incorporating higher orders of memory [ 13 ] in a method proposed in [ 14 ], long-term developments can be tracked reliably.…”
Section: Introductionmentioning
confidence: 99%
“…Previously in [ 14 ], we introduced a random-walk approach for hashtag community detection and a subsequent memory-based matching scheme on temporal networks. We had demonstrated that the lifetime of small communities can be increased with this approach.…”
BackgroundHashtags are widely used for communication in online media. As a condensed version of information, they characterize topics and discussions. For their analysis, we apply methods from network science and propose novel tools for tracing their dynamics in time-dependent data. The observations are characterized by bursty behaviors in the increases and decreases of hashtag usage. These features can be reproduced with a novel model of dynamic rankings.Hashtag communities in timeWe build temporal and weighted co-occurrence networks from hashtags. On static snapshots, we infer the community structure using customized methods. On temporal networks, we solve the bipartite matching problem of detected communities at subsequent timesteps by taking into account higher-order memory. This results in a matching protocol that is robust toward temporal fluctuations and instabilities of the static community detection. The proposed methodology is broadly applicable and its outcomes reveal the temporal behavior of online topics.Modeling topic-dynamicsWe consider the size of the communities in time as a proxy for online popularity dynamics. We find that the distributions of gains and losses, as well as the interevent times are fat-tailed indicating occasional, but large and sudden changes in the usage of hashtags. Inspired by typical website designs, we propose a stochastic model that incorporates a ranking with respect to a time-dependent prestige score. This causes occasional cascades of rank shift events and reproduces the observations with good agreement. This offers an explanation for the observed dynamics, based on characteristic elements of online media.
“…The basic assumption still being that 'the greater the probability of two elements co-occurring in the same article, the more strongly they are related' (Chavalarias & Cointet, 2013, p. 2). So far, these 'elements' have included for instance organizations and firms (Vaughan & You, 2010); hyperlinks (Boulton, Devriendt, Brunn, Derudder, & Witlox, 2011;Salvini & Fabrikant, 2016) or even hashtags (Lorenz, Wolf, Braun, Djurdjevac Conrad, & Hövel, 2018) in addition to the key words characterizing scientific fieldssee Peris, Meijers, and van Ham (forthcoming) for such a scientometric approach for the field of urban systems research. The increasing availability of crowdsourced 'big data' and technological advances have provided an important impetus to the application of co-occurrence analysis.…”
Section: Using Co-occurrences To Determine Inter-city Relationshipsmentioning
While there is consensus that network embeddedness of cities is of great importance for their development, the precise effect is difficult to assess because of a lack of consistent information on relations between cities. This paper presents, applies and evaluates a rather novel method to establish the strength of relationships between places, a method we refer to as 'the toponym co-occurrence method'. This approach builds the urban system on the basis of co-occurrences of place names in a text corpus. We innovate by exploiting a so far unparalleled amount of data, namely the billions of web pages contained in the commoncrawl web archive, and by applying the method also to small places that tend to be ignored by other methods. The entire settlement system of the Netherlands is consequently explored. In addition, we innovatively apply machine learning techniques to classify these relations. Much attention is paid to solving biases deriving from place name disambiguation. Gravity modelling is employed to assess the resulting spatial organization of the Netherlands. It turns out that the gravity model fits very well with the pattern of relationships between places as found in digital space, which contributes to our assessment that the toponym co-occurrence method is a solid proxy for relationships in real space. Using the method, it is established that the relationships in the Randstad region, by many considered a coherent metropolitan entity, are actually somewhat less strong than expected. In contrast, historically important, but nowadays small cities in the periphery tend to have maintained their prominent position in the pattern of relationships. Suburban, relatively new places in the shadow of a larger city tend to be weakly related to other places. Several suggestions to further improve the method, in particular the classification of relationships, are discussed.
“…As these clusters are only dependent on the state of the network at time t, it is then necessary to match the communities at different t with some similarity measures, e.g. Jaccard based Morini et al (2017), Lorenz et al (2018), Greene et al (2010), core-node Wang et al (2008). Methods in category (b) define clusters at t depending on current and past states of the network.…”
Description of temporal networks and detection of dynamic communities have been hot topics of research for the last decade. However, no consensual answers to these challenges have been found due to the complexity of the task. Static communities are not well defined objects, and adding a temporal dimension renders the description even more difficult. In this article, we propose a coherent temporal clustering method: the Best Combination of Local Communities (BCLC). Our method aims at finding a good balance between two conflicting objectives : closely following the short time evolution by finding optimal partitions at each time step and temporal smoothness, which privileges historical continuity. We test our algorithm on two bibliographic data sets by comparing their mesoscale dynamic description to those derived from a (static) simple clustering algorithm applied over the whole data set. We show that our clustering algorithm can reveal more complex dynamics than the simple approach and reach a good agreement with expert's knowledge.
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