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اطلاعات دوره: 
  • سال: 

    2024
  • دوره: 

    10
تعامل: 
  • بازدید: 

    35
  • دانلود: 

    0
چکیده: 

Graph representation learning aims to extract Embedding vectors for graph nodes, such that similar nodes have close vectors in the Embedding space. Existing methods often measure node similarity based on their common neighbors, which may overlook nodes with similar structures in different parts of the graph. We want to capture the Structural similarity of nodes that are not adjacent in the graph. To this end, we propose struc2vec+k, a new method that extends the basic struc2vec method. The basic method considers two nodes to be Structurally similar if their nodes in the first, second, third, and subsequent layers are similar. The proposed method also takes into account the connection between layers, and aggregates the information of two consecutive layers. For instance, for the second layer, the information of the first-and second-layer nodes are aggregated. This aggregation is based on the inter-layer connections. The aggregation can be done up to the k-th layer, which explains the name of the method. We show that the proposed method achieves good accuracy in numerical experiments.

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اطلاعات دوره: 
  • سال: 

    2021
  • دوره: 

    7
تعامل: 
  • بازدید: 

    129
  • دانلود: 

    0
چکیده: 

In parallel with the development of online social networks, the number of active users in these media is increased, which mainly use these media as a tool to share their opinions and obtaining information. Propagation of influence on social networks arises from a common social behavior called "mouth-to-mouth" diffusion among society members. The Influence Maximization (IM) problem aims to select a minimum set of users in a social network to maximize the spread of influence. In this paper, we propose a method in order to solve the IM problem on social media that uses the network Embedding concept to learn the feature vectors of nodes. In the first step of the proposed method, we extract a Structural feature vector for each node by network Embedding. Afterward, according to the similarity between the vectors, the seed set of influential nodes is selected in the second step. The investigation of the results obtained from applying the proposed method on the real datasets indicates its significant advantage against its alternatives. Specifically, the two properties of being submodular and monotonic in the proposed method, which lead to an optimal solution with the ratio of approximation, make this method considered a tool with high potential in order to address the IM problem.

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بازدید 129

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نویسندگان: 

KOUSHESH M.R.

اطلاعات دوره: 
  • سال: 

    2014
  • دوره: 

    40
  • شماره: 

    1
  • صفحات: 

    125-155
تعامل: 
  • استنادات: 

    0
  • بازدید: 

    356
  • دانلود: 

    0
چکیده: 

For a given measure space (X, B, m) we construct all measure spaces (Y, C, l) in which (X, B, m) is embeddable. The construction is modeled on the ultrafilter construction of the Stone-Cech compactification of a completely regular topological space.Under certain conditions the construction simplifies. Examples are given when this simplification occurs.

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مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
اطلاعات دوره: 
  • سال: 

    2023
  • دوره: 

    18
  • شماره: 

    2
  • صفحات: 

    185-198
تعامل: 
  • استنادات: 

    0
  • بازدید: 

    48
  • دانلود: 

    0
چکیده: 

One of the important features of an interconnection network is its ability to efficiently simulate programs or parallel algorithms written for other architectures. Such a simulation problem can be mathematically formulated as a graph Embedding problem. In this paper we compute the lower bound for dilation and congestion of Embedding onto wheel-like networks. Further, we compute the exact dilation of Embedding wheellike networks into hypertrees, proving that the lower bound obtained is sharp. Again, we compute the exact congestion of Embedding windmill graphs into circulant graphs, proving that the lower bound obtained is sharp. Further, we compute the exact wirelength of Embedding wheels and fans into 1, 2-fault hamiltonian graphs. Using this we estimate the exact wirelength of Embedding wheels and fans into circulant graphs, generalized Petersen graphs, augmented cubes, crossed cubes, Möbius cubes, twisted cubes, twisted n-cubes, locally twisted cubes, generalized twisted cubes, odd-dimensional cube connected cycle, hierarchical cubic networks, alternating group graphs, arrangement graphs, 3-regular planer hamiltonian graphs, star graphs, generalised matching networks, fully connected cubic networks, tori and 1-fault traceable graphs.

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اطلاعات دوره: 
  • سال: 

    1396
  • دوره: 

    4
  • شماره: 

    1
  • صفحات: 

    17-28
تعامل: 
  • استنادات: 

    0
  • بازدید: 

    794
  • دانلود: 

    224
چکیده: 

با پنهان سازی بیت های محرمانه در یک سیستم نهان نگاری تصویر، تصاویر حامل اطلاعات محرمانه دچار اعوجاج می شوند. این امر منجر به احتمال ظن دشمن به وجود پیام محرمانه در این تصاویر می شود. جاسازی ماتریسی از طریق تقسیم تصویر پوششی به بلوک های با طول مشخص و اعمال تغییرات محدود در هر بلوک، به کاهش اعوجاج ناشی از پنهان سازی اطلاعات محرمانه کمک می کند. با این حال، استفاده از این ساختار منجر به محدود شدن ظرفیت اطلاعات قابل پنهان سازی در تصاویر پوششی می شود. در این مقاله، ظرفیت سیستم جاسازی ماتریسی از طریق اصلاح روش فن افزایش می یابد. با افزایش حداکثر تعداد تغییرات در یک بلوک به ازای پنهان سازی تعداد بیت محرمانه مشخص در هر بلوک، طول بلوک تصویر پوششی کاهش یافته و از این رو، نرخ جاسازی افزایش می یابد. همچنین به ازای ظرفیت یکسان، اعوجاج کاهش و بازده جاسازی افزایش می یابد. در این روش، تصاویر حامل با کیفیت مطلوب و PSNR بالا حاصل می شود. از طرفی، روش پیشنهادی منجر به افزایش مقاومت در برابر پنهان شکنی می شود. این امر، احتمال ظن دشمن به وجود پیام محرمانه در تصاویر حامل را کاهش داده و امنیت را افزایش می دهد. نتایج شبیه سازی، حاکی از عملکرد مناسب روش پیشنهادی در مقایسه با سایر روش های موجود در این زمینه است.

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نویسندگان: 

اطلاعات دوره: 
  • سال: 

    2020
  • دوره: 

    8
  • شماره: 

    -
  • صفحات: 

    439-453
تعامل: 
  • استنادات: 

    1
  • بازدید: 

    76
  • دانلود: 

    0
کلیدواژه: 
چکیده: 

شاخص‌های تعامل:   مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

بازدید 76

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مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
نویسندگان: 

KHARRAZI M. | SENCAR M. | MEMON H.

اطلاعات دوره: 
  • سال: 

    2006
  • دوره: 

    -
  • شماره: 

    -
  • صفحات: 

    117-121
تعامل: 
  • استنادات: 

    1
  • بازدید: 

    134
  • دانلود: 

    0
کلیدواژه: 
چکیده: 

شاخص‌های تعامل:   مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

بازدید 134

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نویسندگان: 

DEMYANOVICH YURI K.

نشریه: 

MATHEMATICAL SCIENCES

اطلاعات دوره: 
  • سال: 

    2012
  • دوره: 

    6
  • شماره: 

    -
  • صفحات: 

    1-7
تعامل: 
  • استنادات: 

    0
  • بازدید: 

    291
  • دانلود: 

    0
چکیده: 

Purpose: The aims of the paper are to obtain necessary and sufficient conditions of existence and smoothness for non-polynomial spline spaces of fifth order, to establish the uniqueness of the Bφ-spline spaces in the class C4 among mentioned spaces (under condition of fixed grid), and to prove the Embedding of the Bφ-spline spaces corresponding to embedded grids.Methods: In the paper, the approximation relations with initial grid and with complete chain of vectors are applied to obtain the minimal spline spaces. Usage of locally orthogonal chain of vectors gives opportunity to construct special approximation relations from which the initial space of Bφ splines is constructed.Results: Deletion of a knot from initial grid gives a new grid, and as result, a new space of Bφ splines is embedded in the initial space mentioned above.Conclusions: Consequent deletion of the knots (one by one) generates the sequence of the embedded spaces of Bφ splines. Obtained results are successfully proved. They may be applied to spline-wavelet decompositions.

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نویسندگان: 

MAHDAVI ALI | RAHMATI FARHAD

اطلاعات دوره: 
  • سال: 

    2017
  • دوره: 

    6
  • شماره: 

    2
  • صفحات: 

    1-6
تعامل: 
  • استنادات: 

    0
  • بازدید: 

    267
  • دانلود: 

    0
چکیده: 

Letf ¹1; 3 be a positive integer. We prove that there exists a numerical semigroup S with Embedding dimension three such that f is the Frobenius number of S. We also show that the same fact holds for affine semigroups in higher dimensional monoids.

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بازدید 267

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اطلاعات دوره: 
  • سال: 

    2021
  • دوره: 

    53
  • شماره: 

    1
  • صفحات: 

    67-78
تعامل: 
  • استنادات: 

    0
  • بازدید: 

    52
  • دانلود: 

    0
چکیده: 

A novel smart vaccination method is proposed in this paper to distribute a limited number of vaccines among the people of a large community, such as a country, consisting of smaller communities like cities or provinces. The proposed method is comprised of two phases; A vaccine allocation phase and a targeted vaccination phase. In the first phase, the available vaccines are allocated to the communities based on demographics and the effectiveness of each type of vaccine. In the second phase, each community is modelled as a contact graph, and the vaccines available to the community are administered to the individuals whose vaccination has the greatest impact on breaking the chain of transmission. As a result of utilizing the Node2Vec graph Embedding algorithm, the complexity of the proposed method increases linearly with the number of people in the community, as opposed to common centrality based methods, the complexities of which increase with the square or cube of the number of individuals. Furthermore, the proposed method can distribute multiple types of vaccines with different probabilities of effectiveness. The performance of the proposed method is comparable to the common centrality based vaccination methods, while its complexity is lower. The results of the simulation show a 20% decrease in the peak number of infected individuals.

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بازدید 52

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