Received: FebruAccepted: AugPublished: September 15, 2022Ĭopyright: © 2022 Gomes Ferreira et al. (2022) On network backbone extraction for modeling online collective behavior. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation.Ĭitation: Gomes Ferreira CH, Murai F, Silva APC, Trevisan M, Vassio L, Drago I, et al. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. Each technique has its specific assumptions and procedure to extract the backbone. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. However, only a fraction of edges contribute to the actual investigation. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. Nevertheless, that weakness must be considered when planning a network topology.Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. In fact, many of the other LAN backbone topologies also introduce a single point of failure into the LAN. Use it as a traffic aggregator for LAN-level resources, like WAN facilities, and not indiscriminately, like a bridge.Ĭollapsed backbones, like the one shown in Figure 3-15, have another flaw: They introduce a single point of failure in the LAN. In other words, the IP switch performs inter-LAN routing at wire speeds!Ĭare should be taken when designing collapsed backbone LANs to absolutely minimize the amount of traffic that must cross the router. Today, Layer 3 LAN switches are available that duplicate the functionality of the router in a collapsed backbone without duplicating its slow performance. Consequently, collapsed backbones might actually introduce a performance penalty not present with Layer 2-only LAN backbone solutions. Therefore, in comparison to purely hardware devices, such as hubs and switches, routers are slow. As fast as routers may be, they are still software driven. Subsequently, simple network tasks among the members of a workgroup are likely to traverse the router. Instead, the users are scattered far and wide, which means that there is a good chance that they will be found on different LANs interconnected via a collapsed backbone router. An important consideration in collapsed backbone topologies is that user communities are seldom conveniently distributed throughout a building.
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