Learning Clusters through Information Diffusion

Prokhorenkova, Liudmila; Tikhonov, Alexey; Litvak, Nelly

doi:10.1145/3308558.3313560

Cited by 12 publications

(20 citation statements)

References 28 publications

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“…In this paper, we compare two clusterization algorithms: spectral clustering and Louvain [6]. Both of them were used in recent papers for community prediction in networks [7,8]. Looking at modularity estimation (Table 1), we see that Louvain clustering gives not stable clusters.…”

Section: Resultsmentioning

confidence: 99%

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Community Detection Based on the Nodes Role in a Network: The Telegram Platform Case

Tikhomirova

Makarov

2021

Lecture Notes in Computer Science

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The paper studies the community detection problem on Telegram channels. The dataset is received from TGStat service and includes the information of 58k forwards between 100 politician Telegram channels. We implement modern clustering approaches to solve the problem of missing social links. Our study is based on a combination of structural features with strategy-based attributes, including indicators designed according to the nodes' role in a network. Authors provide ten novel indicators, which are calculated for each network's member per each message in order to vectorize a Telegram channel with regard to its strategy of information spread and the way of contacting other channels. Authors construct a metric-based graph of channel relations and cluster channels representations using network science techniques. Obtained results are studied using quantitative and qualitative analysis showing promising results in applying joint network-based and KPI-based models for the stated problem.

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Section: Resultsmentioning

confidence: 99%

“…Each estimation belongs to a node reaction for a message of our dataset, so the vector's length is 29 902. This way, we can compare users in a network from a cascade perspective [7].…”

Section: Features Extractionmentioning

confidence: 99%

Community Detection Based on the Nodes Role in a Network: The Telegram Platform Case

Tikhomirova

Makarov

2021

Lecture Notes in Computer Science

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“…For example, the Bayesian inference models assume that both exact activation times and edges are unknown and required to be inferred [40,41]. Besides, the network inference can be integrated into other frameworks to accomplish certain tasks, such as clustering [42] and recommendation system [43]. There are also a bunch of methods focus on representation learning under information cascades, which can also be used for inferring networks [44,45].…”

Section: Network Inferencementioning

confidence: 99%

Latent Network Embedding via Adversarial Auto-encoders

Lei,

Shi,

Niu

2021

Preprint

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Graph auto-encoders have proved to be useful in network embedding task. However, current models only consider explicit structures and fail to explore the informative latent structures cohered in networks. To address this issue, we propose a latent network embedding model based on adversarial graph auto-encoders. Under this framework, the problem of discovering latent structures is formulated as inferring the latent ties from partial observations. A latent transmission matrix that describes the strengths of existing edges and latent ties is derived based on influence cascades sampled by simulating diffusion processes over networks. Besides, since the inference process may bring extra noises, we introduce an adversarial training that works as regularization to dislodge noises and improve the model robustness. Extensive experiments on link prediction and node classification tasks show that the proposed model achieves superior results compared with baseline models.

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“…Recently, there is a growing interest in community detection for dynamic networks [7][8][9][10][11][12]. This stands in contrast to classic community detection where the network is assumed to be directly observed.…”

Section: Introductionmentioning

confidence: 99%

“…When only agent states can be observed, rather than edge sets, community detection is much harder. In [7,8,12], statistical inference methods are used to address community detection in diffusion processes, while spectral methods are introduced in [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Detecting Communities in a Gossip Model with Stubborn Agents

Fang

et al. 2021

Preprint

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We consider a community detection problem in a gossip model, where agents randomly interact pairwise, with stubborn agents never changing their states. It is assumed that the agents can be divided into two communities based on their interaction probability with others. Such a model can illustrate how disagreement and opinion fluctuation arise in a social network. The considered problem is twofold: to infer which community each agent belongs to, and to estimate interaction probabilities between agents, by only observing their state evolution. First, stability and limit theorems of the model are derived. An integrated detection and estimation algorithm is then proposed to infer the two communities and to estimate the interaction probabilities, based on agent states. It is verified that the community detector of the algorithm converges in finite time, and the interaction estimator converges almost surely. In addition, non-asymptotic property is obtained for the former, and convergence rate is analyzed for the latter. Simulations are presented to illustrate the performance of the proposed algorithm.

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Learning Clusters through Information Diffusion

Cited by 12 publications

References 28 publications

Community Detection Based on the Nodes Role in a Network: The Telegram Platform Case

Community Detection Based on the Nodes Role in a Network: The Telegram Platform Case

Latent Network Embedding via Adversarial Auto-encoders

Detecting Communities in a Gossip Model with Stubborn Agents

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