Guided sampling for large graphs

Yousuf, Muhammad Irfan; Kim, Suhyun

doi:10.1007/s10618-020-00683-y

Cited by 12 publications

(6 citation statements)

References 35 publications

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“…Average clustering coefficient (ACC) defined as represents how close a node’s neighbors are to forming a clique 32 – 35 , where and denotes the average of the number of links between two neighbors of -degree nodes. A related distribution characteristic is clustering coefficient distribution 32 defined as versus .…”

Section: Discussionmentioning

confidence: 99%

Sampling unknown large networks restricted by low sampling rates

Jiao

2024

Sci Rep

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Graph sampling plays an important role in data mining for large networks. Specifically, larger networks often correspond to lower sampling rates. Under the situation, traditional traversal-based samplings for large networks usually have an excessive preference for densely-connected network core nodes. Aim at this issue, this paper proposes a sampling method for unknown networks at low sampling rates, called SLSR, which first adopts a random node sampling to evaluate a degree threshold, utilized to distinguish the core from periphery, and the average degree in unknown networks, and then runs a double-layer sampling strategy on the core and periphery. SLSR is simple that results in a high time efficiency, but experiments verify that the proposed method can accurately preserve many critical structures of unknown large scale-free networks with low sampling rates and low variances.

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Section: Discussionmentioning

confidence: 99%

Sampling unknown large networks restricted by low sampling rates

Jiao

2024

Sci Rep

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“…The proposed scheme allows some degrees of generalization: in fact, plain random walks can be replaced by extraction procedures based on cliques, graphlets or other types of random walks [81]- [86]; the classifier can as well be personalised; the graph dissimilarity does not necessarily have to be the nBMF and other GEDs can be employed instead [14], [74]. On a higher level, the proposed scheme can be personalized towards virtually any structured input domain, provided that one can extract candidate information granules and fill B (e.g., k-mers in case of sequences) and define a suitable dissimilarity measure for matching (e.g., the Levenshtein distance between k-mers).…”

Section: Discussionmentioning

confidence: 99%

An Enhanced Filtering-Based Information Granulation Procedure for Graph Embedding and Classification

Martino

2021

IEEE Access

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Granular Computing is a powerful information processing paradigm for synthesizing advanced pattern recognition systems in non-conventional domains. In this paper, a novel procedure for the automatic synthesis of suitable information granules is proposed. The procedure leverages a joint sensitivity-vs-specificity score that accounts the meaningfulness of candidate information granules for each class considered in the classification problem at hand. Only statistically relevant granules are retained for a graph embedding procedure towards a geometric space, in which standard classification systems can be used without alterations. Performance tests have been carried out by considering open access datasets of fully labelled graphs with arbitrarily complex nodes and/or edges attributes that, by definition, must rely on inexact graph matching procedures to quantify dissimilarities. Two variants of the procedure are investigated: a standard variant, which aims at automatically finding suitable information granules for solving the classification problem as a whole, and a class-specific metric learning variant, in which the optimization procedure is performed in a class-aware fashion. In the latter case, each class will have its own set of information granules, along with the corresponding parameters defining distinct instances of the dissimilarity measure. Computational results show that the proposed algorithm is able to outperform the vast majority of current approaches for graph classification, while at the same time returning a grey-box model, interpretable by field-experts.

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“…For future works, we plan to investigate other types of sampling in the recommendation module, such as sampling a subgraph [ 50 ]. Random walks can be adopted to generate a walking sequence with most frequent visited nodes, which can emphasize the important neighbors in the recommendation module.…”

Section: Discussionmentioning

confidence: 99%

MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph

Dong

Wang

et al. 2021

PLoS ONE

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To alleviate the data sparsity and cold start problems for collaborative filtering in recommendation systems, side information is usually leveraged by researchers to improve the recommendation performance. The utility of knowledge graph regards the side information as part of the graph structure and gives an explanation for recommendation results. In this paper, we propose an enhanced multi-task neighborhood interaction (MNI) model for recommendation on knowledge graphs. MNI explores not only the user-item interaction but also the neighbor-neighbor interactions, capturing a more sophisticated local structure. Besides, the entities and relations are also semantically embedded. And with the cross&compress unit, items in the recommendation system and entities in the knowledge graph can share latent features, and thus high-order interactions can be investigated. Through extensive experiments on real-world datasets, we demonstrate that MNI outperforms some of the state-of-the-art baselines both for CTR prediction and top-N recommendation.

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Guided sampling for large graphs

Cited by 12 publications

References 35 publications

Sampling unknown large networks restricted by low sampling rates

Sampling unknown large networks restricted by low sampling rates

An Enhanced Filtering-Based Information Granulation Procedure for Graph Embedding and Classification

MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph

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