Diffusion Archaeology for Diffusion Progression History Reconstruction

Sefer, Emre; Kingsford, Carl

doi:10.1109/icdm.2014.135

Cited by 11 publications

(20 citation statements)

References 22 publications

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“…Pseudocode for delayed-bfs is given in Algorithm 3. The variable Q represents the bfs queue while D represents a sorted array with the terminals at which bfs has been delayed; the terminals in D are sorted in add v to D; 14 foreach v in D in chronological order do 15 if t(v) = t cur then break;…”

Section: Delayed Bfs Algorithmmentioning

confidence: 99%

Reconstructing a cascade from temporal observations

Xiao

Rozenshtein

Tatti

et al. 2018

Proceedings of the 2018 SIAM International Conference on Data Mining

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Given a subset of active nodes in a network can we reconstruct the cascade that has generated these observations? This is a problem that has been studied in the literature, but here we focus in the case that temporal information is available about the active nodes. In particular, we assume that in addition to the subset of active nodes we also know their activation time.We formulate this cascade-reconstruction problem as a variant of a Steiner-tree problem: we ask to find a tree that spans all reported active nodes while satisfying temporal-consistency constraints. We present three approximation algorithms. The best algorithm in terms of quality achieves a O( √ k)-approximation guarantee, where k is the number of active nodes, while the most efficient algorithm has linearithmic running time, making it scalable to very large graphs.We evaluate our algorithms on real-world networks with both simulated and real cascades. Our results indicate that utilizing the available temporal information allows for more accurate cascade reconstruction. Furthermore, our objective leads to finding the "backbone" of the cascade and it gives solutions of high precision.

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Section: Delayed Bfs Algorithmmentioning

confidence: 99%

Reconstructing a cascade from temporal observations

Xiao

Rozenshtein

Tatti

et al. 2018

Proceedings of the 2018 SIAM International Conference on Data Mining

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“…Given a collection of sparse observations of coexisting information diffusions of M memes, {O (1) , O (2) , · · · , O (M ) }, where O (m) is the observation of E (m) , m = 1, 2, · · · , M , i.e., O (m) ⊂ E (m) , we want to infer the complete coexisting information diffusions { E (1) , E (2) , · · · , E (M ) }.…”

Section: B Problem Statementmentioning

confidence: 99%

“…We build the Coexisting Diffusions Tensor (CDT) with the sparse observations of M coexisting information diffusions, {O (1) , O (2) , · · · , O (M ) }. A CDT A ∈ R N ×N ×M ×Q consists of 4 modes which respectively represent N source nodes, N destination nodes, M memes that concurrently diffuse, and Q time points.…”

Section: A Coexisting Diffusions Tensormentioning

confidence: 99%

“…Note that a CDT is a 4th-order tensor and its slice at time point t q , A * * * q , 1 ≤ q ≤ Q, is a 3rd-order tensor, as we have defined in Section 3.1 1) Sliding Time Window And Sub-tensor: Let T = t 1 , t 2 , · · · , t Q be a sequence of Q time points considered, and then a sliding time window over T and its corresponding sub-tensor can be defined as follows: For example, in Fig. 2, there are 3 sliding time windows, [1,3], [2,5], and [3,6], and their corresponding sub-tensors are A [1,3] , A [2,5] , and A [3,6] . It is obvious that successive sub-tensors are overlapped, i.e., if A [s ,q ] is the successive sub-tensor of A [s,q] , they share the slices from A * * * s to A * * * q .…”

Section: B Time Window Based Parallel Decomposition Algorithm (Twpda)mentioning

confidence: 99%

“…For example, in Fig. 2, A [2,5] is the successive subtensor of A [1,3] , and they share the slices A * * * 2 and A * * * 3 . Similarly, A [3,6] is the successive sub-tensor of A [2,5] , and they share the slices from A * * * 3 to A * * * 5 .…”

Section: B Time Window Based Parallel Decomposition Algorithm (Twpda)mentioning

confidence: 99%

See 2 more Smart Citations

Collaborative Inference of Coexisting Information Diffusions

Sun

Cong

Yang

et al. 2017

2017 IEEE International Conference on Data Mining (ICDM)

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Recently, diffusion history inference has become an emerging research topic due to its great benefits for various applications, whose purpose is to reconstruct the missing histories of information diffusion traces according to incomplete observations. The existing methods, however, often focus only on single information diffusion trace, while in a realworld social network, there often coexist multiple information diffusions over the same network. In this paper, we propose a novel approach called Collaborative Inference Model (CIM) for the problem of the inference of coexisting information diffusions. By exploiting the synergism between the coexisting information diffusions, CIM holistically models multiple information diffusions as a sparse 4th-order tensor called Coexisting Diffusions Tensor (CDT) without any prior assumption of diffusion models, and collaboratively infers the histories of the coexisting information diffusions via a low-rank approximation of CDT with a fusion of heterogeneous constraints generated from additional data sources. To improve the efficiency, we further propose an optimal algorithm called Time Window based Parallel Decomposition Algorithm (TWPDA), which can speed up the inference without compromise on the accuracy by utilizing the temporal locality of information diffusions. The extensive experiments conducted on real world datasets and synthetic datasets verify the effectiveness and efficiency of CIM and TWPDA.

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History Path Reconstruction Analysis of Topic Diffusion on Microblog

Song

Huang

et al. 2016

Recent Developments in Intelligent Systems and Interactive Applications

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Diffusion Archaeology for Diffusion Progression History Reconstruction

Cited by 11 publications

References 22 publications

Reconstructing a cascade from temporal observations

Reconstructing a cascade from temporal observations

Collaborative Inference of Coexisting Information Diffusions

History Path Reconstruction Analysis of Topic Diffusion on Microblog

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