Processing math: 99%
Research article Special Issues

Integrating artificial intelligence with network evolution theory for community behavior prediction in dynamic complex systems

  • Communication networks, such as social and collaborative networks, are characterized by a highly dynamic, constantly changing environment. This makes the analysis of such networks, such as the formation of communities, challenging. The adaptive temporal graph neural network (AT-GNN) was introduced here to overcome these challenges by incorporating temporal segmentation, feature extraction, and attention mechanisms. Based on two large-scale datasets, the Stanford Network Analysis Project (SNAP) and the Digital Bibliography and Library Project (DBLP), the AT-GNN model considers structural and temporal features for predicting community behaviors. Temporal segmentation was done through clustering while using node and edge attribute extraction. The preprocessing stage involved embedding layers, attention mechanisms, and recurrent layers. These components enabled the AT-GNN model to adjust the weight of essential relationships through dynamic networks, enhancing the explainability of community changes. A comparison was made between the proposed model and best-performing models, showing improved predictive accuracy of 98%, precision of 92%, recall of 95%, and F1-score of 93%. This work emphasizes the scalability, flexibility, and dynamism of the AT-GNN model and offers a starting point for studying dynamic systems. Future work will extend to graphs in continuous time and to enormously large networks, improving the model's effectiveness in real-time dynamic networks. These developments highlight the applicability of AT-GNN in various real-world settings, such as social, biological, and organizational networks.

    Citation: Yongyan Zhao, Jian Li. Integrating artificial intelligence with network evolution theory for community behavior prediction in dynamic complex systems[J]. AIMS Mathematics, 2025, 10(2): 2042-2063. doi: 10.3934/math.2025096

    Related Papers:

    [1] Choukri Derbazi, Hadda Hammouche . Caputo-Hadamard fractional differential equations with nonlocal fractional integro-differential boundary conditions via topological degree theory. AIMS Mathematics, 2020, 5(3): 2694-2709. doi: 10.3934/math.2020174
    [2] Abdelkader Amara . Existence results for hybrid fractional differential equations with three-point boundary conditions. AIMS Mathematics, 2020, 5(2): 1074-1088. doi: 10.3934/math.2020075
    [3] Zaid Laadjal, Fahd Jarad . Existence, uniqueness and stability of solutions for generalized proportional fractional hybrid integro-differential equations with Dirichlet boundary conditions. AIMS Mathematics, 2023, 8(1): 1172-1194. doi: 10.3934/math.2023059
    [4] Naimi Abdellouahab, Keltum Bouhali, Loay Alkhalifa, Khaled Zennir . Existence and stability analysis of a problem of the Caputo fractional derivative with mixed conditions. AIMS Mathematics, 2025, 10(3): 6805-6826. doi: 10.3934/math.2025312
    [5] Muhammed Jamil, Rahmat Ali Khan, Kamal Shah, Bahaaeldin Abdalla, Thabet Abdeljawad . Application of a tripled fixed point theorem to investigate a nonlinear system of fractional order hybrid sequential integro-differential equations. AIMS Mathematics, 2022, 7(10): 18708-18728. doi: 10.3934/math.20221029
    [6] Ala Eddine Taier, Ranchao Wu, Naveed Iqbal . Boundary value problems of hybrid fractional integro-differential systems involving the conformable fractional derivative. AIMS Mathematics, 2023, 8(11): 26260-26274. doi: 10.3934/math.20231339
    [7] Yige Zhao, Yibing Sun, Zhi Liu, Yilin Wang . Solvability for boundary value problems of nonlinear fractional differential equations with mixed perturbations of the second type. AIMS Mathematics, 2020, 5(1): 557-567. doi: 10.3934/math.2020037
    [8] Bashir Ahmad, Ahmed Alsaedi, Ymnah Alruwaily, Sotiris K. Ntouyas . Nonlinear multi-term fractional differential equations with Riemann-Stieltjes integro-multipoint boundary conditions. AIMS Mathematics, 2020, 5(2): 1446-1461. doi: 10.3934/math.2020099
    [9] Kishor D. Kucche, Sagar T. Sutar, Kottakkaran Sooppy Nisar . Analysis of nonlinear implicit fractional differential equations with the Atangana-Baleanu derivative via measure of non-compactness. AIMS Mathematics, 2024, 9(10): 27058-27079. doi: 10.3934/math.20241316
    [10] Mohamed Hannabou, Muath Awadalla, Mohamed Bouaouid, Abd Elmotaleb A. M. A. Elamin, Khalid Hilal . One class class of coupled system fractional impulsive hybrid integro- differential equations. AIMS Mathematics, 2024, 9(7): 18670-18687. doi: 10.3934/math.2024908
  • Communication networks, such as social and collaborative networks, are characterized by a highly dynamic, constantly changing environment. This makes the analysis of such networks, such as the formation of communities, challenging. The adaptive temporal graph neural network (AT-GNN) was introduced here to overcome these challenges by incorporating temporal segmentation, feature extraction, and attention mechanisms. Based on two large-scale datasets, the Stanford Network Analysis Project (SNAP) and the Digital Bibliography and Library Project (DBLP), the AT-GNN model considers structural and temporal features for predicting community behaviors. Temporal segmentation was done through clustering while using node and edge attribute extraction. The preprocessing stage involved embedding layers, attention mechanisms, and recurrent layers. These components enabled the AT-GNN model to adjust the weight of essential relationships through dynamic networks, enhancing the explainability of community changes. A comparison was made between the proposed model and best-performing models, showing improved predictive accuracy of 98%, precision of 92%, recall of 95%, and F1-score of 93%. This work emphasizes the scalability, flexibility, and dynamism of the AT-GNN model and offers a starting point for studying dynamic systems. Future work will extend to graphs in continuous time and to enormously large networks, improving the model's effectiveness in real-time dynamic networks. These developments highlight the applicability of AT-GNN in various real-world settings, such as social, biological, and organizational networks.



    Hybrid differential equations have been considered more important and served as special cases of dynamical systems. Dhage and Lakshmikantham [1] were the first to study ordinary hybrid differential equation and studied the existence of solutions for this boundary value problem. In recent years, with the wide study of fractional differential equations, the theory of hybrid fractional differential equations were also studied by several researchers, see [2,3,4,5,6,7,8,9,10] and the references therein.

    Zhao et al. [2] studied existence and uniqueness results for the following hybrid differential equations involving Riemann-Liouville fractional derivative

    Dq0+(x(t)f(t,x(t)))=g(t,x(t)),  a.e.tJ=[0,T]
    x(0)=0,

    where 0<q<1,fC(J×RR{0}) and gC(J×R,R).

    Zidane Baitiche et al. [11] considered the following boundary value problem of nonlinear fractional hybrid differential equations involving Caputo's derivative

    CDα0+(x(t)f(t,x(μ(t))))=g(t,x(μ(t))),  tI=[0,1]
    a[x(t)f(t,x(μ(t)))]|t=0+b[x(t)f(t,x(μ(t)))]|t=1=c,

    where 0<α1,CDα0+ is the Caputo fractional derivative. fC(I×RR{0}),gC(I×R,R).

    As we all known, the hadamard fractional differential equations are also popular in the literature, see [12,13,14,15,16], so some authors began to study the theory of fractional hybrid differential equation of hadamard type.

    Zidane Baitiche et al. [17] studied the existence of solutions for fractional hybrid differential equation of hadamard type with dirichlet boundary conditions

    HDα(x(t)f(t,x(t)))=g(t,x(t)),  1<t<e, 1<α2,
    x(1)=0,   x(e)=0,

    where 1<α2, HDα is the Hadamard fractional derivative, fC([1,e]×RR{0}) and gC([1,e]×R,R).

    In [18], M. Jamil et al. discussed the existence result for the boundary value problem of hybrid fractional integro-differential equations involving Caputo's derivative given by

    CDα(CDωu(t)mi=1Iβifi(t,u(t))g(t,u(t)))=h(t,u(t),Iγu(t)),  tJ=[0,1],
    u(0)=0, Dωu(0)=0, u(1)=δu(η),  0<δ<1,  0<η<1,

    where CDα is the Caputo fractional derivative of order α, CDω is the Caputo fractional derivative of order ω, 0<α1, 1<ω2.

    In order to analyze fractional differential equations in a generic way, a fractional derivative with respect to another function called φ-Caputo derivative was proposed [19].

    By mixing idea of the above works, we derived an existence result for the nonlocal boundary value problems of hybrid φ-Caputo fractional integro-differential equations

    CDα φ(CDβ φu(t)mi=1Iωi φfi(t,u(t),Iμ1 φu(t),,Iμn φu(t))g(t,u(t),Iγ1 φu(t),,Iγp φu(t)))=h(t,u(t)),tJ=[0,1], (1.1)
    u(0)=0, CDβ φu(0)=0, u(1)=kj=1δju(ξj), (1.2)

    where 0<α1, 1<β2, CDα φ is the φ-Caputo fractional derivative of order α, CDβ φ is the φ-Caputo fractional derivative of order β, the function φ: [0,1]R is a strictly increasing function such that φC2[0,1] with φ(x)>0 for all x[0,1], Iμ φ denote the φ-Riemann-Liouville fractional integral of order μ, gC(J×Rp+1,R{0}), hC(J×R,R) and fiC(J×Rn+1,R) with fi(0,0,,0n+1)=0, wi>0, i=1,2,,m, μ1,,μn>0 and γ1,,γp>0, 0<δj<1, j=1,2,,k, 0<ξ1<ξ2<<ξk<1.

    It is notable that the fractional hybrid integro-differential equation presented in this paper is the novel in the sense that the fractional derivative with respect to another function called φ-Caputo fractional derivative. Note that the hybrid fractional integro-differential equations involving Caputo's derivative in [18] is a special case of our hybrid φ-Caputo fractional integro-differential equations with φ(t)=t. Moreover, all dependent functions fi and g in our paper are in the form of multi-term. Furthermore, our problem is more general than the work in [8], as we consider the problem with multi-point boundary conditions, while the authors in [8] only investigated two-point boundary condition.

    The organization of this work is as follows. Section 2 contains some preliminary facts that we need in the sequel. In section 3, we present the solution for the hybrid fractional integro-differential equation (1.1), (1.2) and then prove our main existence results. Finally, we illustrate the obtained results by an example.

    In the following and throughtout the text, a>0 is a real, x:[a,b]R an integrable function and φC2[a,b] an increasing function such that with φ(t)0 for all t[a,b].

    Definition 2.1 The φ-Riemann-Liouville fractional integral of x of order α is defined as follows

    Iα φa+x(t):=1Γ(α)taφ(s)(φ(t)φ(s))α1x(s)ds.

    Definition 2.2 The φ-Riemann-Liouville fractional derivative of x of order α is defined as follows

    Dα φa+x(t):=(1φ(t)ddt)nInα φa+x(t)=1Γ(nα)(1φ(t)ddt)ntaφ(s)(φ(t)φ(s))nα1x(s)ds,

    here n=[α]+1.

    Remark 2.1 Let α,β>0, then the relation holds

    Iα φa+Iβ φa+x(t)=Iα+β φa+x(t).

    Definition 2.3 Let α>0 and xCn1[a,b], the φ-Caputo fractional derivative of x of order α is defined as follows

    CDα φa+x(t):=Dα φa+[x(t)n1k=0x[k]φ(a)k!(φ(t)φ(a))k], n=[α]+1 for αN, n=α for αN,

    where x[k]φ(t):=(1φ(t)ddt\bigamma)kx(t).

    Theorem 2.1 [20] Let x:[a,b]R. The following results hold:

    1. If xC[a,b], then CDα φa+Iα φa+x(t)=x(t);

    2. If xCn1[a,b], then

    Iα φ Ca+Dα φa+x(t)=x(t)n1k=0x[k]φ(a)k!(φ(t)φ(a))k.

    Lemma 2.2 [18] Let S be a nonempty, convex, closed, and bounded set such that SE, and let A:EE and B:SE be two operators which satisfy the following :

    (H1)A is contraction;

    (H2)B is compact and continuous, and

    (H3)u=Au+Bv, vSuS.

    Then there exists a solution of the operator equation u=Au+Bu.

    Let E=C(J,R) be a Banach space equipped with the norm

    u=suptJ|u(t)|   and  (uv)(t)=u(t)v(t),   tJ.

    Then E is a Banach algebra with the above norm and multiplication.

    Lemma 3.1 Suppose that α,β,ωi,i=1,2,,m,γi,i=1,2,,p,μi,i=1,2,,n,δj,ξj,j=1,2,,k and functions g,h,fi,i=1,2,,m satisfy problem (1.1), (1.2). Then the unique solution of (1.1), (1.2) is given by

    u(t)=t0(φ(t)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds+mi=1Iωi+β φfi(t,u(t),Iμ1 φu(t),,Iμn φu(t))+φ(t)φ(0)kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))[10(φ(1)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds+mi=1Iωi+β φfi(1,u(1),Iμ1 φu(1),,Iμn φu(1))kj=1δjξj0(φ(ξj)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτdskj=1δjmi=1Iωi+β φfi(ξj,u(ξj),Iμ1 φu(ξj),,Iμn φu(ξj))], (3.1)

    where

    Iωi+β φfi(t,u(t),Iμ1 φu(t),,Iμn φu(t))=t0(φ(t)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))ds;
    Iωi+β φfi(1,u(1),Iμ1 φu(1),,Iμn φu(1))=10(φ(1)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))ds;
    Iωi+β φfi(ξj,u(ξj),Iμ1 φu(ξj),,Iμn φu(ξj))=ξj0(φ(ξj)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))ds.

    Proof. We apply φ-Riemann-Liouville fractional integral Iα φ on both sides of (1.1), by Theorem 2.1, we have

    CDβ φu(t)mi=1Iωi φfi(t,u(t),Iμ1 φu(t),,Iμn φu(t))g(t,u(t),Iγ1 φu(t),,Iγp φu(t))=Iα φh(t,u(t))+c0,

    then by u(0)=0, CDβ φu(0)=0, fi(0,0,,0n+1)=0, we get c0=0. i.e,

    CDβ φu(t)=g(t,u(t),Iγ1 φu(t),,Iγp φu(t))t0(φ(t)φ(s))α1Γ(α)φ(s)h(s,u(s))ds+mi=1Iωi φfi(t,u(t),Iμ1 φu(t),,Iμn φu(t)). (3.2)

    Apply again fractional integral Iβ φ on both sides of (3.2) and by Theorem 2.1, we get

    u(t)=t0(φ(t)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds+mi=1Iωi+β φfi(t,u(t),Iμ1 φu(t),,Iμn φu(t))+c1+c2(φ(t)φ(0)), (3.3)

    u(0)=0, fi(0,0,,0n+1)=0 yield c1=0, thus equation (3.3) is reduced to

    u(t)=t0(φ(t)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds+mi=1Iωi+β φfi(t,u(t),Iμ1 φu(t),,Iμn φu(t))+c2(φ(t)φ(0)), (3.4)

    specially.

    u(1)=10(φ(1)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds+mi=1Iωi+β φfi(1,u(1),Iμ1 φu(1),,Iμn φu(1))+c2(φ(1)φ(0)),
    u(ξj)=ξj0(φ(ξj)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds+mi=1Iωi+β φfi(ξj,u(ξj),Iμ1 φu(ξj),,Iμn φu(ξj))+c2(φ(ξj)φ(0)),

    from u(1)=kj=1δju(ξj), we have

    c2=1kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))[10(φ(1)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds+mi=1Iωi+β φfi(1,u(1),Iμ1 φu(1),,Iμn φu(1))kj=1δjξj0(φ(ξj)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτdskj=1δjmi=1Iωi+β φfi(ξj,u(ξj),Iμ1 φu(ξj),,Iμn φu(ξj))].

    Consequently, we can get the desired result. The proof is completed.

    Theorem 3.2 Suppose that functions gC(J×Rp+1,R{0}), hC(J×R,R) and fiC(J×Rn+1,R) with fi(0,0,,0n+1)=0. Furthermore, assume that

    (C1) there exist bounded mapping σ:[0,1]R+, λ:[0,1]R+ such that

    |g(t,k1,k2,,kp+1)g(t,k1,k2,,kp+1)|σ(t)p+1i=1|kiki|

    for tJ and (k1,k2,,kp+1),(k1,k2,,kp+1)Rp+1, and

    |h(t,u)h(t,v)|λ(t)|uv| for tJ and u,vR;

    (C2) there exist ϕi,Ω,χC(J,R+),i=1,2,,m such that

    |fi(t,k1,k2,,kn+1)|ϕi(t),  (t,k1,k2,,kn+1)J×Rn+1,
    |h(t,u)|Ω(t),  (t,u)J×R,
    |g(t,k1,k2,,kp+1)|χ(t),  (t,k1,k2,,kp+1)J×Rp+1;

    (C3) there exists r>0 such that

    (1+(φ(1)φ(0))(1+kj=1δj)|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|)(χΩ(φ(1)φ(0))αΓ(α+1)(φ(1)φ(0))βΓ(β+1)+mi=1ϕi(φ(1)φ(0))ωi+βΓ(ωi+β+1))r; (3.5)
    (χλ+Ωσp+1i=1(φ(1)φ(0))γiΓ(γi+1))(φ(1)φ(0))αΓ(α+1)(1+(φ(1)φ(0))(1+kj=1δj)|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|)(φ(1)φ(0))βΓ(β+1)<1, (3.6)

    where Ω=sup0t1|Ω(t)|, ϕi=sup0t1|ϕi(t)|, i=1,2,,p, χ=sup0t1|χ(t)|, λ=sup0t1|λ(t)|, σ=sup0t1|σ(t)|.

    Then the hybrid problem (1.1), (1.2) has at least one solution.

    Proof. Define a subset S of E as

    S={uE: ur},

    where r satisfies inequality (3.5). Clearly S is closed, convex and bounded subset of the Banach space E. Define two operators A:EE by

    Au(t)=t0(φ(t)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds+φ(t)φ(0)kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))10(φ(1)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds(φ(t)φ(0))kj=1δjkj=1δj(φ(ξj)φ(0))(φ(1)φ(0))ξj0(φ(ξj)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds, (3.7)
    Bu(t)=mi=1t0(φ(t)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))ds+φ(t)φ(0)kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))mi=110(φ(1)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))ds(φ(t)φ(0))kj=1δjkj=1δj(φ(ξj)φ(0))(φ(1)φ(0))mi=1ξj0(φ(ξj)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))ds. (3.8)

    Then u(t) is a solution of problem (1.1), (1.2) if and only if u(t)=Au(t)+Bu(t). We shall show that the operators A and B satisfy all the conditions of Lemma 2.2. We split the proof into several steps.

    Step 1. We first show that A is a contraction mapping. Let u(t),v(t)S, we write

    G(s)=g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτg(s,v(s),Iγ1 φv(s),,Iγp φv(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,v(τ))dτ,

    then by (C1) we have

    |G(s)|=|g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτg(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,v(τ))dτ+g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,v(τ))dτg(s,v(s),Iγ1 φv(s),,Iγp φv(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,v(τ))dτ||g(s,u(s),Iγ1 φu(s),,Iγp φu(s))|s0(φ(s)φ(τ))α1Γ(α)φ(τ)|h(τ,u(τ))h(τ,v(τ))|dτ+s0(φ(s)φ(τ))α1Γ(α)φ(τ)|h(τ,v(τ))|dτ|g(s,u(s),Iγ1 φu(s),,Iγp φu(s))g(s,v(s),Iγ1 φv(s),,Iγp φv(s))|χλuv(φ(s)φ(0))αΓ(α+1)+Ω(φ(s)φ(0))αΓ(α+1)σp+1i=1(φ(s)φ(0))γiΓ(γi+1)uv(χλ+Ωσp+1i=1(φ(1)φ(0))γiΓ(γi+1))(φ(1)φ(0))αΓ(α+1)uv,

    thus we have

    |Au(t)Av(t)|t0(φ(t)φ(s))β1Γ(β)φ(s)G(s)ds+φ(t)φ(0)|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|10(φ(1)φ(s))β1Γ(β)φ(s)G(s)ds+φ(t)φ(0)|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|kj=1δjξj0(φ(ξj)φ(s))β1Γ(β)φ(s)G(s)ds(χλ+Ωσp+1i=1(φ(1)φ(0))γiΓ(γi+1))(φ(1)φ(0))αΓ(α+1)(1+(φ(1)φ(0))(1+kj=1δj)|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|)(φ(1)φ(0))βΓ(β+1)uv,

    which implies

    Au(t)Av(t)[(χλ+Ωσp+1i=1(φ(1)φ(0))γiΓ(γi+1))(φ(1)φ(0))αΓ(α+1)(1+(φ(1)φ(0))(1+kj=1δj)|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|)(φ(1)φ(0))βΓ(β+1)]uv,

    in view of (3.6), this shows that A is a contraction mapping.

    Step 2. The operator B is compact and continuous on S.

    First, we show that B is continuous on S. Let {un} be a sequence of functions in S converging to a function uS. Then by Lebesgue dominated convergence theorem,

    limnBun(t)=limn[mi=1t0(φ(t)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,un(s),Iμ1 φun(s),,Iμn φun(s))ds+φ(t)φ(0)kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))mi=110(φ(1)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,un(s),Iμ1 φun(s),,Iμn φun(s))ds(φ(t)φ(0))kj=1δjkj=1δj(φ(ξj)φ(0))(φ(1)φ(0))mi=1ξj0(φ(ξj)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,un(s),Iμ1 φun(s),,Iμn φun(s))ds].=mi=1t0(φ(t)φ(s))ωi+β1Γ(ωi+β)φ(s)limnfi(s,un(s),Iμ1 φun(s),,Iμn φun(s))ds+φ(t)φ(0)kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))mi=110(φ(1)φ(s))ωi+β1Γ(ωi+β)φ(s)limnfi(s,un(s),Iμ1 φun(s),,Iμn φun(s))ds(φ(t)φ(0))kj=1δjkj=1δj(φ(ξj)φ(0))(φ(1)φ(0))mi=1ξj0(φ(ξj)φ(s))ωi+β1Γ(ωi+β)φ(s)limnfi(s,un(s),Iμ1 φun(s),,Iμn φun(s))ds=mi=1t0(φ(t)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φ(s),,Iμn φu(s))ds+φ(t)φ(0)kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))mi=110(φ(1)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))ds(φ(t)φ(0))kj=1δjkj=1δj(φ(ξj)φ(0))(φ(1)φ(0))mi=1ξj0(φ(ξj)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))ds=Bu(t).

    This shows that B is continuous on S. It is sufficient to show that B(S) is a uniformly bounded and equicontinuous set in E.

    First, we note that

    |Bu(t)|mi=1t0(φ(t)φ(s))ωi+β1Γ(ωi+β)φ(s)|fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))|ds+φ(t)φ(0)|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|mi=110(φ(1)φ(s))ωi+β1Γ(ωi+β)φ(s)|fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))|ds+(φ(t)φ(0))kj=1δj|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|mi=1ξj0(φ(ξj)φ(s))ωi+β1Γ(ωi+β)φ(s)|fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))|dsmi=1ϕi(φ(1)φ(0))ωi+βΓ(ωi+β+1)+(φ(1)φ(0))(1+kj=1δj)|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|mi=1ϕi(φ(1)φ(0))ωi+βΓ(ωi+β+1)=(1+(φ(1)φ(0))(1+kj=1δj)|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|)mi=1ϕi(φ(1)φ(0))ωi+βΓ(ωi+β+1).

    This shows that B is uniformly bounded on S.

    Next, we show that B is an equicontinuous set in E. Let t1,t2J with t1<t2 and uS. Then we have

    |Bu(t2)Bu(t1)|=|mi=1t20(φ(t2)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))dsmi=1t10(φ(t1)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))ds+φ(t2)φ(t1)kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))mi=110(φ(1)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))ds(φ(t2)φ(t1))kj=1δjkj=1δj(φ(ξj)φ(0))(φ(1)φ(0))mi=1ξj0(φ(ξj)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,u(s),Iμ1 φu(s),,Iμn φu(s))ds|mi=1ϕiΓ(ωi+β)[|t10[(φ(t2)φ(s))ωi+β1(φ(t1)φ(s))ωi+β1]φ(s)ds+t2t1[(φ(t2)φ(s))ωi+β1φ(s)ds|+φ(t2)φ(t1)|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|10(φ(1)φ(s))ωi+β1φ(s)ds+(φ(t2)φ(t1))kj=1δj|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|ξj0(φ(ξj)φ(s))ωi+β1φ(s)ds]mi=1ϕiΓ(ωi+β+1)[|(φ(t2)φ(0))ωi+β(φ(t1)φ(0))ωi+β|+φ(t2)φ(t1)|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|(φ(1)φ(0))ωi+β+(φ(t2)φ(t1))kj=1δj|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|(φ(ξj)φ(0))ωi+β].

    Let h(t)=(φ(t)φ(0))ωi+β. Then h is continuously differentiable function. Consequently, for all t1,t2[0,1], without loss of generality, let t1<t2, then there exist positive constants M such that

    |h(t2)h(t1)|=|h(ξ)||t2t1|M|t2t1|,   ξ(t1,t2).

    On the other hand, for φC[0,1], thus there exist positive constants N such that |φ(t2)φ(t1)|=|φ(ξ)||t2t1|N|t2t1|,   ξ(t1,t2), from which we deduce

    |Bu(t2)Bu(t1)|0    as  t2t10.

    Therefore, it follows from the Arzela-Ascoli theorem that B is a compact operator on S.

    Step 3. Next we show that hypothesis (H3) of Lemma 2.2 is satisfied. Let vS, then we have

    |u(t)|=|Au(t)+Bv(t)||Au(t)|+|Bv(t)||t0(φ(t)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds+φ(t)φ(0)kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))10(φ(1)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds(φ(t)φ(0))kj=1δjkj=1δj(φ(ξj)φ(0))(φ(1)φ(0))ξj0(φ(ξj)φ(s))β1Γ(β)φ(s)g(s,u(s),Iγ1 φu(s),,Iγp φu(s))s0(φ(s)φ(τ))α1Γ(α)φ(τ)h(τ,u(τ))dτds|+|mi=1t0(φ(t)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,v(s),Iμ1 φv(s),,Iμn φv(s))ds+φ(t)φ(0)kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))mi=110(φ(1)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,v(s),Iμ1 φv(s),,Iμn φv(s))ds(φ(t)φ(0))kj=1δjkj=1δj(φ(ξj)φ(0))(φ(1)φ(0))mi=1ξj0(φ(ξj)φ(s))ωi+β1Γ(ωi+β)φ(s)fi(s,v(s),Iμ1 φv(s),,Iμn φv(s))ds|(1+(φ(1)φ(0))(1+kj=1δj)|kj=1δj(φ(ξj)φ(0))(φ(1)φ(0))|)(χΩ(φ(1)φ(0))αΓ(α+1)(φ(1)φ(0))βΓ(β+1)+mi=1ϕi(φ(1)φ(0))ωi+βΓ(ωi+β+1))r,

    which implies ur and so uS.

    Thus all the conditions of Lemma 2.2 are satisfied and hence the operator equation u=Au+Bu has a solution in S. In consequence, the problem (1.1), (1.2) has a solution on J. This completes the proof.

    In this section, we provide an example to illustrate our main result.

    Example 4.1 Consider the following hybrid φ-Caputo fractional integro-differential equations

    CD12 t4(CD32 t4u(t)2i=1Iωit4fi(t,u(t),I13t4u(t),I43t4u(t))14t2(|u(t)|1+|u(t)|+|I14t4u(t)|1+|I14t4u(t)|+sinI12t4u(t)))=25cos(t4)(|u(t)||u(t)|+1),  tJ=[0,1], (4.1)
    u(0)=0, CD32 t4u(0)=0, u(1)=13u(13), (4.2)

    where

    2i=1Iωit4fi(t,u(t),I13t4u(t),I43t4u(t))=I13t4(t[|u(t)|1+|u(t)|+sin(I13t4u(t))+cos(I43t4u(t))])+I23t4(t10[|u(t)|1+|u(t)|+arctan(I13t4u(t))+sin(I43t4u(t))]). (4.3)

    We note that α=12,β=32,m=2,n=2,p=2,k=1,δ=13,ξ=13,ω1=13,ω2=23,μ1=13,μ2=43,γ1=14,γ2=12,φ(t)=t4,

    f1(t,u(t),I13t4u(t),I43t4u(t))=t[|u(t)|1+|u(t)|+sin(I13t4u(t))+cos(I43t4u(t))],
    f2(t,u(t),I13t4u(t),I43t4u(t))=t10[|u(t)|1+|u(t)|+arctan(I13t4u(t))+sin(I43t4u(t))],
    g(t,u(t),I14t4u(t),I12t4u(t))=14t2(|u(t)|1+|u(t)|+|I14t4u(t)|1+|I14t4u(t)|+sinI12t4u(t)),
    h(t,u(t))=25cos(t4)(|u(t)||u(t)|+1).

    Thus we have

    |g(t,u(t),I14t4u(t),I12t4u(t))g(t,v(t),I14t4v(t),I12t4v(t))|σ(t)[1+t14Γ(54)+t12Γ(32)]|u(t)v(t)|=t24[1+t14Γ(54)+t12Γ(32)]|u(t)v(t)|,
    |h(t,u(t))h(t,v(t))|=25cos(t4)|u(t)v(t)|.

    Therefore,

    σ=sup0t1|σ(t)|=sup0t1t24[1+t14Γ(54)+t12Γ(32)]=14(1+1Γ(54)+1Γ(32))=14(1+10.9064+10.8862)=0.8079;
    λ=sup0t1|λ(t)|=sup0t125cos(t4)=0.4;
    ϕ1=sup0t1|ϕ1(t)|=sup0t1t(1+1+1)=3;
    ϕ2=sup0t1|ϕ2(t)|=sup0t1t10(1+π2+1)=110×3.57=0.357;
    Ω=sup0t1|Ω(t)|=sup0t125cos(t4)=0.4;
    χ=sup0t1|χ(t)|=sup0t1t24(1+1+1)=34=0.75.

    Choose r>0.5, then we have

    (1+14×4329)[0.75×0.4×(14)12Γ(32)×(14)32Γ(52)+3×(14)116Γ(176)+0.357×(14)136Γ(196)]=0.4016r.

    Moreover,

    \biggl(0.75\times 0.4+0.4\times 0.8079\times \biggl(\frac{(\frac{1}{4})^{\frac{1}{4}}}{\Gamma(\frac{5}{4})}+\frac{(\frac{1}{4})^{\frac{1}{2}}}{\Gamma(\frac{3}{2})}\biggr)\biggr) \frac{(\frac{1}{4})^{\frac{1}{2}}}{\Gamma(\frac{3}{2})}\biggl(1+\frac{\frac{1}{4}\times \frac{4}{3}}{\frac{2}{9}}\biggr)\frac{(\frac{1}{4})^{\frac{3}{2}}}{\Gamma(\frac{5}{2})} = 0.097 \lt 1.

    Now, by using Theorem 3.2, it is deduced that the fractional hybrid integro-differential problem (4.1), (4.2) has a solution.

    Hybrid fractional integro-differential equations have been considered more important and served as special cases of dynamical systems. In this paper, we introduced a new class of the hybrid \varphi -Caputo fractional integro-differential equations. By using famous hybrid fixed point theorem due to Dhage, we have developed adequate conditions for the existence of at least one solution to the hybrid problem (1.1), (1.2). The respective results have been verified by providing a suitable example.

    We express our sincere thanks to the anonymous reviewers for their valuable comments and suggestions. This work is supported by the Natural Science Foundation of Tianjin (No.(19JCYBJC30700)).

    The authors declare no conflict of interest in this paper.



    [1] M. Pósfai, A. L. Barabási, Network science, Cambridge: Cambridge University Press, 2016.
    [2] S. Fortunato, D. Hric, Community detection in networks: A user guide, Phys. Rep., 659 (2016), 1–44. https://doi.org/10.1016/j.physrep.2016.09.002 doi: 10.1016/j.physrep.2016.09.002
    [3] Y. Wu, L. Pan, LSTEG: An evolutionary game model leveraging deep reinforcement learning for privacy behavior analysis on social networks, Inform. Sciences, 2024, 120842. https://doi.org/10.1016/j.ins.2024.120842 doi: 10.1016/j.ins.2024.120842
    [4] W. Jiang, J. Luo, Graph neural network for traffic forecasting: A survey, Expert Syst. Appl., 207 (2022), 117921. https://doi.org/10.1016/j.eswa.2022.117921 doi: 10.1016/j.eswa.2022.117921
    [5] P. Holme, J. Saramäki, Temporal networks, Phys. Rep., 519 (2012), 97–125. https://doi.org/10.1016/j.physrep.2012.03.001 doi: 10.1016/j.physrep.2012.03.001
    [6] G. Rossetti, R. Cazabet, Community discovery in dynamic networks: A survey, ACM Comput. Surv., 51 (2018), 1–37. https://doi.org/10.1145/3172867 doi: 10.1145/3172867
    [7] Z. Qiu, Y. Yin, Y. Yuan, Y. Chen, Research on credit regulation mechanism of E-commerce platform based on evolutionary game theory, J. Syst. Sci. Syst. Eng., 2024, 1–30. https://doi.org/10.1007/s11518-024-5603-2 doi: 10.1007/s11518-024-5603-2
    [8] A. L. Barabási, R. Albert, Emergence of scaling in random networks, Science, 286 (1999), 509–512. https://doi.org/10.1126/science.286.5439.509 doi: 10.1126/science.286.5439.509
    [9] P. Holme, B. J. Kim, Growing scale-free networks with tunable clustering, Phys. Rev. E, 65 (2002), 026107. https://doi.org/10.1103/PhysRevE.65.026107 doi: 10.1103/PhysRevE.65.026107
    [10] A. L. Barabási, Z. N. Oltvai, Network biology: Understanding the cell's functional organization, Nat. Rev. Gene., 5 (2004), 101. https://doi.org/10.1038/nrg1272 doi: 10.1038/nrg1272
    [11] M. A. Saidu, L. S. Shamsudeen, A. K. Muhammad, A. Abdulkadir, Exploring E-commerce opportunities for a better international trading and tax revenue generation: A review for developing countries, J. Sci. Technol. Educ., 10 (2022), 109–24.
    [12] Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, S. Y. Philip, A comprehensive survey on graph neural networks, IEEE T. Neur. Net. Lear., 32 (2020), 4–24. https://doi.org/10.1109/TNNLS.2020.2978386 doi: 10.1109/TNNLS.2020.2978386
    [13] P. Goyal, S. R. Chhetri, A. Canedo, Dyngraph2vec: Capturing network dynamics using dynamic graph representation learning, Knowl.-Based Syst., 187 (2020), 104816. https://doi.org/10.1016/j.knosys.2019.06.024 doi: 10.1016/j.knosys.2019.06.024
    [14] L. Akoglu, H. Tong, D. Koutra, Graph-based anomaly detection and description: A survey, Data Min. Knowl. Disc., 29 (2015), 626–688. https://doi.org/10.1007/s10618-014-0365-y doi: 10.1007/s10618-014-0365-y
    [15] Y. Jiang, B. Ma, X. Wang, G. Yu, P. Yu, Z. Wang, et al., Blockchained federated learning for internet of things: A comprehensive survey, ACM Comput. Surv., 56 (2024), 1–37. https://doi.org/10.1145/3659099 doi: 10.1145/3659099
    [16] A. A. Beni, A. Esmaeili, Biosorption, an efficient method for removing heavy metals from industrial effluents: A Review, Environ. Technol. Inno., 17 (2020), 100503. https://doi.org/10.1016/j.eti.2019.100503 doi: 10.1016/j.eti.2019.100503
    [17] M. McPherson, L. S. Lovin, J. M. Cook, Birds of a feather: Homophily in social networks, Annu. Rev. Sociol., 27 (2001), 415–444. https://doi.org/10.1146/annurev.soc.27.1.415 doi: 10.1146/annurev.soc.27.1.415
    [18] P. Holme, J. Saramaki, Temporal networks, Phys. Rep., 519 (2012), 97–125. https://doi.org/10.1016/j.physrep.2012.03.001 doi: 10.1016/j.physrep.2012.03.001
    [19] G. Rossetti, M. Stella, R. Cazabet, K. Abramski, E. Cau, S. Citraro, et al., Y Social: An LLM-powered social media digital twin, arXiv Preprint, 2024. https://doi.org/10.48550/arXiv.2408.00818 doi: 10.48550/arXiv.2408.00818
    [20] D. C. Nguyen, Q. V. Pham, P. N. Pathirana, M. Ding, A. Seneviratne, Z. Lin, et al., Federated learning for smart healthcare: A survey, ACM Comput. Surv., 55 (2022), 1–37. https://doi.org/10.1145/3501296 doi: 10.1145/3501296
    [21] L. Cai, Z. Chen, C. Luo, J. Gui, J. Ni, D. Li, et al., Structural temporal graph neural networks for anomaly detection in dynamic graphs, In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021, 3747–3756. https://doi.org/10.1145/3459637.3481955
    [22] X. Zhu, Y. Zhang, H. Ying, H. Chi, G. Sun, L. Zeng, Modeling epidemic dynamics using graph attention-based spatial temporal networks, Plos one, 19 (2024), e0307159. https://doi.org/10.1145/3459637.3481955 doi: 10.1145/3459637.3481955
    [23] H. Taherdoost, M. Madanchian, AI advancements: Comparison of innovative techniques, AI, 5 (2023), 38–54. https://doi.org/10.3390/ai5010003 doi: 10.3390/ai5010003
    [24] D. Jin, Z. Yu, P. Jiao, S. Pan, D. He, J. Wu, et al., A survey of community detection approaches: From statistical modeling to deep learning, IEEE T. Knowl. Data Eng., 35 (2021), 1149–1170.
    [25] J. L. A. A. Krevl. Stanford Network Analysis Project, Available from: http://snap.stanford.edu/data.
    [26] M. Ley. Digital Bibliography and Library Project, Available from: https://dblp.org/.
    [27] S. Min, Z. Gao, J. Peng, L. Wang, K. Qin, B. Fang, STGSN—a spatial-temporal graph neural network framework for time-evolving social networks, Knowl.-Based Syst., 214 (2021), 106746. https://doi.org/10.1016/j.knosys.2021.106746 doi: 10.1016/j.knosys.2021.106746
    [28] Y. R. Lin, Y. Chi, S. Zhu, H. Sundaram, B. L. Tseng, Analyzing communities and their evolutions in dynamic social networks, ACM T. Knowl. Discov. D., 3 (2009), 1–31. https://doi.org/10.1145/1514888.1514891 doi: 10.1145/1514888.1514891
  • This article has been cited by:

    1. Ayub Samadi, Sotiris K. Ntouyas, Jessada Tariboon, Nonlocal coupled hybrid fractional system of mixed fractional derivatives via an extension of Darbo's theorem, 2021, 6, 2473-6988, 3915, 10.3934/math.2021232
    2. Muath Awadalla, Nazim I. Mahmudov, Hüseyin Işık, On System of Mixed Fractional Hybrid Differential Equations, 2022, 2022, 2314-8888, 1, 10.1155/2022/1258823
    3. Ashwini D. Mali, Kishor D. Kucche, José Vanterler da Costa Sousa, On coupled system of nonlinear Ψ-Hilfer hybrid fractional differential equations, 2021, 0, 1565-1339, 10.1515/ijnsns-2021-0012
    4. Muath Awadalla, Kinda Abuasbeh, Muthaiah Subramanian, Murugesan Manigandan, On a System of ψ-Caputo Hybrid Fractional Differential Equations with Dirichlet Boundary Conditions, 2022, 10, 2227-7390, 1681, 10.3390/math10101681
    5. Hamid Beddani, Moustafa Beddani, Zoubir Dahmani, A new tripled system of hybrid differential equations with φ-Caputo derivatives, 2022, 55, 27044963, 12, 10.20948/mathmontis-2022-55-2
    6. Mohamed Benallou, Hamid Beddani, Moustafa Beddani, Existence of solution for a tripled system of fractional hybrid differential equations with laplacie involving Caputo derivatives, 2024, 5, 2764-0981, e11971, 10.54021/seesv5n2-734
  • Reader Comments
  • © 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(259) PDF downloads(46) Cited by(0)

Figures and Tables

Figures(6)  /  Tables(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog