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Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
[article]
2020
arXiv
pre-print
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It has a temporal self-attention mechanism to model long-range dependencies in the evolution. ...
Most graph neural network models learn embeddings of nodes in static attributed graphs for predictive analysis. Recent attempts have been made to learn temporal proximity of the nodes. ...
It aggregated the information in previous snapshots to the current one using temporal self-attention and employed a multi-task loss function based on attribute inference and link prediction over time. ...
arXiv:2007.13004v1
fatcat:2lqeepd4vrgx7nfxwcazjvg7k4
TD score: Time Aware Domain Similarity based Link Prediction
2022
International Journal for Research in Applied Science and Engineering Technology
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Keywords: Co-authorship network; link prediction; node similarity; global feature; temporal feature; domain similarity. ...
Experiment over co-authorship network reveals that link prediction covering time aware domain similarity is effective and efficient approach than traditional ones. ...
Temporal graph can be created by encoding temporal data into graphs. This will act as a valuable tool for temporal analysis and prediction. ...
doi:10.22214/ijraset.2022.43176
fatcat:qg45ioaconbdjfhq5mvs73irc4
Communities detection and analysis of their dynamics in collaborative networks
2007
2007 2nd International Conference on Digital Information Management
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We apply this approach on the Infocom co-authorship network to determine stable collaborations and evolving communities. ...
Finally, we analyse the impact of the co-authorships relation topology on the formation of the program committee board of the conference. # Author cliques C Associated set S of Years 1 M. ...
To identify evolving communities, we construct the graph G evol on the Infocom co-authorship data. The obtained graph contains 8 692 vertices and 29 972 edges. ...
doi:10.1109/icdim.2007.4444313
dblp:conf/icdim/JdidiaRF07
fatcat:xkoprw6yqnaqvexwf7ikh5xlie
Dual network embedding for representing research interests in the link prediction problem on co-authorship networks
2019
PeerJ Computer Science
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We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. ...
Standard graph feature engineering and network embedding methods were combined for constructing co-author recommender system formulated as LP problem and prediction of future graph structure. ...
of graph-based recommender system predicting links in the co-authorship network and incorporating author research interests in collaborative patterns. ...
doi:10.7717/peerj-cs.172
pmid:33816825
pmcid:PMC7924522
fatcat:cpddqvz7p5ezvbslcbv54ezrzy
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs
[article]
2021
arXiv
pre-print
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less predictive power on the labels. ...
, e.g., social networks with dynamic relationships, or literature citation networks with changing co-authorships. ...
In these datasets, every author is extracted as a node in a graph, and co-authorship determines whether there is a connection between two nodes. ...
arXiv:2110.05598v1
fatcat:cz555loqifbtbp5rptuu6ont7e
Embedding-Based Recommendations on Scholarly Knowledge Graphs
[chapter]
2020
Lecture Notes in Computer Science
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TransE with Soft Margin (TransE-SM) obtains a score of 79.5% Hits@10 for co-authorship link prediction task while the original TransE obtains 77.2%, on the same task. ...
In this paper, KGEs are reconciled with a specific loss function (Soft Margin) and examined with respect to their performance for co-authorship link prediction task on scholarly KGs. ...
, -validating the predicted co-authorship links by a profile check of scholars. ...
doi:10.1007/978-3-030-49461-2_15
fatcat:37ioh4sql5danl4kc4nppa3oza
Love tHy Neighbour: Remeasuring Local Structural Node Similarity in Hypergraph-Derived Networks
[article]
2021
arXiv
pre-print
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Using a combination of the existing graph-based and the proposed hypergraph-based similarity scores as features for a classifier predicts links much better than using the former solely. ...
We compare our scores with graph-based scores (over clique-expansions of hypergraphs into graphs) from the state-of-the-art. ...
co-authorship network from its originally occurring hypergraph (i.e., its higher-order counterpart). ...
arXiv:2111.00256v1
fatcat:z5unh6xkirdm7bdcwc2na7dlly
Dynamic Graph Representation Learning via Self-Attention Networks
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1812.09430v1 fatcat:cddc7ty6pfaobcj2cxeeyfligy
We conduct link prediction experiments on two classes of graphs: communication networks and bipartite rating networks. ...
Previous methods on graph representation learning mainly focus on static graphs, however, many real-world graphs are dynamic and evolve over time. ...
We leave exploring other architectural design choices based on structural and temporal self-attentions as future work. ...
arXiv:1812.09430v2
fatcat:qfilyfvmj5ecjmiknkvoropeqi
ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural Networks
[article]
2021
arXiv
pre-print
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Learning node representations on temporal graphs is a fundamental step to learn real-word dynamic graphs efficiently. ...
learning methods focused generally on static graphs. ...
Secondly our model can be expanded for multi-layer dynamic graph networks and multifeatured graph networks like Co-Authorship dataset. ...
arXiv:2106.11430v1
fatcat:t2r2oycql5gttk3z22qblkrk3q
Link Prediction in Social Networks: the State-of-the-Art
[article]
2014
arXiv
pre-print
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A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. ...
In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. ...
The co-authorship social network is one of the most popular networks in link prediction. Ichise et al. ...
arXiv:1411.5118v2
fatcat:ns5ufekku5hwnotjlfgj2oiaei
Link prediction in social networks: the state-of-the-art
2014
Science China Information Sciences
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A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. ...
In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. ...
The co-authorship social network is one of the most popular networks in link prediction. Ichise et al. ...
doi:10.1007/s11432-014-5237-y
fatcat:x6jd4zwg7fgefjrho4en4rtfgm
Tie Strength in Online Social Networks and its Applications: A Brief Study
[article]
2020
arXiv
pre-print
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In online social network (OSN), understanding the factors bound to the role and strength of interaction(tie) are essential to model a wide variety of network-based applications. ...
The recognition of these interactions can enhance the accuracy of link prediction, improve in ranking of dominants, reliability of recommendation and enhance targeted marketing as decision support system ...
[6] identified tie strength in temporal network of co-authorship relations by measuring edge persistence and evolution of ties over time. ...
arXiv:2002.07608v2
fatcat:2mehz44vhnaovgnrz65o7zodxi
Prediction and ranking algorithms for event-based network data
2005
SIGKDD Explorations
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In contrast, in this paper we focus on the temporal nature of such data. ...
Examples include email traffic, telephone calls, and research publications (interpreted as co-authorship events). ...
Acknowledgements This material is based upon work supported by the National Science Foundation under award number NSF IIS-0083489. ...
doi:10.1145/1117454.1117458
fatcat:k4bty55fq5c5zo6xt4bqc7txzi
Temporal Network Representation Learning via Historical Neighborhoods Aggregation
[article]
2020
arXiv
pre-print
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Then we apply a deep learning model which uses a custom attention mechanism to induce node embeddings that directly capture temporal information in the underlying feature representation. ...
More specifically, we first propose a temporal random walk that can identify relevant nodes in historical neighborhoods which have impact on edge formations. ...
. • DBLP is a co-author network where each edge represents a co-authorship between two authors (nodes). ...
arXiv:2003.13212v1
fatcat:me7k6zbln5cmhnxzoltalskzhu
Temporal Network Representation Learning via Historical Neighborhoods Aggregation
2020
2020 IEEE 36th International Conference on Data Engineering (ICDE)
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Then we apply a deep learning model which uses a custom attention mechanism to induce node embeddings that directly capture temporal information in the underlying feature representation. ...
More specifically, we first propose a temporal random walk that can identify relevant nodes in historical neighborhoods which have impact on edge formations. ...
. • DBLP is a co-author network where each edge represents a co-authorship between two authors (nodes). ...
doi:10.1109/icde48307.2020.00101
dblp:conf/icde/HuangB0ZC20
fatcat:b7nrbfmfrzditgqtznlxvxilxu
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